{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f5498da6-85a6-4a21-b164-84af87f658a3",
   "metadata": {},
   "source": [
    "### 需求分析\n",
    "1. 用户个体消费分析\n",
    "2. 用户整体消费分析（按月）\n",
    "3. 用户消费行为分析\n",
    "4. 用户分层\n",
    "5. 新老、活跃、回流用户分析\n",
    "6. 用户的购买周期\n",
    "7. 用户生命周期\n",
    "8. 复购率、回购率分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f62b953-9189-4d8f-9a9e-f0d4827a6769",
   "metadata": {},
   "outputs": [],
   "source": [
    "#引包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot') # 更改绘图风格，R语言绘图库\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "97818f4d-51c7-4825-a1fb-cc6e773a25e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_dt</th>\n",
       "      <th>product</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19970101</td>\n",
       "      <td>1</td>\n",
       "      <td>11.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>19970112</td>\n",
       "      <td>1</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>19970112</td>\n",
       "      <td>5</td>\n",
       "      <td>77.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>19970102</td>\n",
       "      <td>2</td>\n",
       "      <td>20.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>19970330</td>\n",
       "      <td>2</td>\n",
       "      <td>20.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  order_dt  product  amount\n",
       "0        1  19970101        1   11.77\n",
       "1        2  19970112        1   12.00\n",
       "2        2  19970112        5   77.00\n",
       "3        3  19970102        2   20.76\n",
       "4        3  19970330        2   20.76"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据引入  user_id  用户id  order_dt 订单时间  order_products 购买数量 order_amount 购买金额\n",
    "column = ['user_id','order_dt','product','amount']\n",
    "df = pd.read_table('data/CDNOW_master.txt',names=column,sep=r'\\s+') # sep:'\\s+ 匹配任意空格'\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db21af57-9d23-487f-b576-106289a56b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_dt</th>\n",
       "      <th>product</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>69659.000000</td>\n",
       "      <td>6.965900e+04</td>\n",
       "      <td>69659.000000</td>\n",
       "      <td>69659.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11470.854592</td>\n",
       "      <td>1.997228e+07</td>\n",
       "      <td>2.410040</td>\n",
       "      <td>35.893648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6819.904848</td>\n",
       "      <td>3.837735e+03</td>\n",
       "      <td>2.333924</td>\n",
       "      <td>36.281942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.997010e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5506.000000</td>\n",
       "      <td>1.997022e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11410.000000</td>\n",
       "      <td>1.997042e+07</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>25.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17273.000000</td>\n",
       "      <td>1.997111e+07</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>43.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>23570.000000</td>\n",
       "      <td>1.998063e+07</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1286.010000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id      order_dt       product        amount\n",
       "count  69659.000000  6.965900e+04  69659.000000  69659.000000\n",
       "mean   11470.854592  1.997228e+07      2.410040     35.893648\n",
       "std     6819.904848  3.837735e+03      2.333924     36.281942\n",
       "min        1.000000  1.997010e+07      1.000000      0.000000\n",
       "25%     5506.000000  1.997022e+07      1.000000     14.490000\n",
       "50%    11410.000000  1.997042e+07      2.000000     25.980000\n",
       "75%    17273.000000  1.997111e+07      3.000000     43.700000\n",
       "max    23570.000000  1.998063e+07     99.000000   1286.010000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d4768a68-cc5c-4156-9feb-f871d883348b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 69659 entries, 0 to 69658\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   user_id   69659 non-null  int64  \n",
      " 1   order_dt  69659 non-null  int64  \n",
      " 2   product   69659 non-null  int64  \n",
      " 3   amount    69659 non-null  float64\n",
      "dtypes: float64(1), int64(3)\n",
      "memory usage: 2.1 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29c85f2c-dd57-4323-be40-3160f5e0769a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 69659 entries, 0 to 69658\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   user_id   69659 non-null  int64         \n",
      " 1   order_dt  69659 non-null  int64         \n",
      " 2   product   69659 non-null  int64         \n",
      " 3   amount    69659 non-null  float64       \n",
      " 4   date      69659 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(1), int64(3)\n",
      "memory usage: 2.7 MB\n",
      "None\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_dt</th>\n",
       "      <th>product</th>\n",
       "      <th>amount</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19970101</td>\n",
       "      <td>1</td>\n",
       "      <td>11.77</td>\n",
       "      <td>1997-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>19970112</td>\n",
       "      <td>1</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1997-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>19970112</td>\n",
       "      <td>5</td>\n",
       "      <td>77.00</td>\n",
       "      <td>1997-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>19970102</td>\n",
       "      <td>2</td>\n",
       "      <td>20.76</td>\n",
       "      <td>1997-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>19970330</td>\n",
       "      <td>2</td>\n",
       "      <td>20.76</td>\n",
       "      <td>1997-03-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  order_dt  product  amount       date\n",
       "0        1  19970101        1   11.77 1997-01-01\n",
       "1        2  19970112        1   12.00 1997-01-12\n",
       "2        2  19970112        5   77.00 1997-01-12\n",
       "3        3  19970102        2   20.76 1997-01-02\n",
       "4        3  19970330        2   20.76 1997-03-30"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'] = pd.to_datetime(df['order_dt'],format='%Y%m%d')\n",
    "print(df.info())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "583260c0-aa6d-4795-b959-029430edcb0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 69659 entries, 0 to 69658\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   user_id     69659 non-null  int64         \n",
      " 1   order_dt    69659 non-null  int64         \n",
      " 2   product     69659 non-null  int64         \n",
      " 3   amount      69659 non-null  float64       \n",
      " 4   date        69659 non-null  datetime64[ns]\n",
      " 5   month       69659 non-null  int32         \n",
      " 6   year_month  69659 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(1), int32(1), int64(3)\n",
      "memory usage: 3.5 MB\n",
      "None\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_dt</th>\n",
       "      <th>product</th>\n",
       "      <th>amount</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year_month</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19970101</td>\n",
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       "      <td>11.77</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>19970112</td>\n",
       "      <td>1</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1997-01-12</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>19970112</td>\n",
       "      <td>5</td>\n",
       "      <td>77.00</td>\n",
       "      <td>1997-01-12</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>19970102</td>\n",
       "      <td>2</td>\n",
       "      <td>20.76</td>\n",
       "      <td>1997-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>19970330</td>\n",
       "      <td>2</td>\n",
       "      <td>20.76</td>\n",
       "      <td>1997-03-30</td>\n",
       "      <td>3</td>\n",
       "      <td>1997-03-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  order_dt  product  amount       date  month year_month\n",
       "0        1  19970101        1   11.77 1997-01-01      1 1997-01-01\n",
       "1        2  19970112        1   12.00 1997-01-12      1 1997-01-01\n",
       "2        2  19970112        5   77.00 1997-01-12      1 1997-01-01\n",
       "3        3  19970102        2   20.76 1997-01-02      1 1997-01-01\n",
       "4        3  19970330        2   20.76 1997-03-30      3 1997-03-01"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['month'] = df['date'].dt.month\n",
    "df['year_month'] = df['date'].dt.to_period('M').dt.start_time\n",
    "print(df.info())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c9f9109-a57d-4e9a-af00-4c4b4d12aef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#按月份统计产品购买数量、金额、次数、人数\n",
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(221)\n",
    "gp_month = df.groupby(by=\"year_month\")['product'].sum().plot()\n",
    "plt.title('Product purchase quantity in month')\n",
    "plt.subplot(222)\n",
    "gp_month = df.groupby(by=\"year_month\")['amount'].sum().plot()\n",
    "plt.title('amounts in month')\n",
    "plt.subplot(223)\n",
    "gp_month = df.groupby(by=\"year_month\")['user_id'].count().plot()\n",
    "plt.title('number of orders')\n",
    "plt.subplot(224)\n",
    "#gp_month = df.groupby(by=\"year_month\")['user_id'].apply(lambda x:len(x.drop_duplicates())).plot()  # 方案一 使用lambda表达式\n",
    "gp_month = df.groupby(by=\"year_month\")['user_id'].nunique().plot()\n",
    "plt.title('Monthly Consumer Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0f2ef76c-060b-4f95-9540-9aac7024d894",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "商品平均单价 15.501922553255737\n",
      "             amount       product  average_amount\n",
      "count  23570.000000  23570.000000    23570.000000\n",
      "mean     106.080426      7.122656       15.501923\n",
      "std      240.925195     16.983531        8.006495\n",
      "min        0.000000      1.000000        0.000000\n",
      "25%       19.970000      1.000000       12.665000\n",
      "50%       43.395000      3.000000       14.262500\n",
      "75%      106.475000      7.000000       15.960000\n",
      "max    13990.930000   1033.000000      283.970000\n",
      "用户数量 23570\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amount</th>\n",
       "      <th>product</th>\n",
       "      <th>average_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.77</td>\n",
       "      <td>1</td>\n",
       "      <td>11.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89.00</td>\n",
       "      <td>6</td>\n",
       "      <td>14.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>156.46</td>\n",
       "      <td>16</td>\n",
       "      <td>9.778750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.50</td>\n",
       "      <td>7</td>\n",
       "      <td>14.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>385.61</td>\n",
       "      <td>29</td>\n",
       "      <td>13.296897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23566</th>\n",
       "      <td>36.00</td>\n",
       "      <td>2</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23567</th>\n",
       "      <td>20.97</td>\n",
       "      <td>1</td>\n",
       "      <td>20.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23568</th>\n",
       "      <td>121.70</td>\n",
       "      <td>6</td>\n",
       "      <td>20.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23569</th>\n",
       "      <td>25.74</td>\n",
       "      <td>2</td>\n",
       "      <td>12.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23570</th>\n",
       "      <td>94.08</td>\n",
       "      <td>5</td>\n",
       "      <td>18.816000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23570 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         amount  product  average_amount\n",
       "user_id                                 \n",
       "1         11.77        1       11.770000\n",
       "2         89.00        6       14.833333\n",
       "3        156.46       16        9.778750\n",
       "4        100.50        7       14.357143\n",
       "5        385.61       29       13.296897\n",
       "...         ...      ...             ...\n",
       "23566     36.00        2       18.000000\n",
       "23567     20.97        1       20.970000\n",
       "23568    121.70        6       20.283333\n",
       "23569     25.74        2       12.870000\n",
       "23570     94.08        5       18.816000\n",
       "\n",
       "[23570 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用户个体消费分析\n",
    "\n",
    "gp_user = df.groupby(by='user_id')[['amount','product']].sum()\n",
    "gp_user['average_amount'] = gp_user['amount']/gp_user['product']\n",
    "print('商品平均单价',gp_user['average_amount'].mean())\n",
    "print(gp_user.describe())\n",
    "print('用户数量',len(gp_user))\n",
    "gp_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "665765ac-5050-4267-85b4-d0d819366cf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制产品购买数量与金额的散点图(可以看出用户购买商品的平均单价)\n",
    "\n",
    "df.plot(kind='scatter',x='product',y='amount')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "32fb4e0b-4e3c-4f2d-8d0e-858c91b28b8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YoYkTJ2rWrFnKzMzUiy++WGEsX331lV5//XX16NFDt9xyS433AQAaovqusSRp3bp16tevnx566CGVlpbqmWeeqfD3vTqlpaWaP3++Tpw4oTFjxjgcGHnnnXe0du1a3XrrrXrkkUfk5eWlZ599Vj///LPxnLS0NL3xxhsaPXq0fve73+mHH37Qhx9+KOl8U+u5555TQUGB5syZoylTpujbb7/V+vXrjddv3LhRGzduVGxsrObMmaPmzZvr5ZdfNuLV/d7WxMsvv6x///vfmj59uu688069//77l5Wj//znP3rhhRfUuXNnPf744/rNb36jlStX6l//+pfD8/7yl7+oXbt2evjhh9WmTRu9+uqrOnfunG644QbFx8fr4YcfliQ9/PDDio+P15QpU2o8hilTpig+Pl5DhgxRUFCQUbvccMMNkqQuXbrIz89PKSkpxmt2796t4uJi9evXr0bbsNvtev7557Vnzx5NmTJFkydP1nfffacXXnjBeE75doOCgjRkyBDj8aXmwKrMSy+9pPT0dM2aNUv333+/vvnmG4da8sMPP9SGDRs0btw4Pfroo7rmmmscPhOHDh3S/Pnz1aZNGz366KMaMWKEEhMTtXXrVoftXOr9qCuvvPKKzpw5ozlz5mjixInasWOHPv74YyNOfYTLwZlSaLKOHTumBQsW6Nprr5V0vsFz6NAhSeeLBIvFot///vdydXVVRESEDhw4oM8//1yDBw+u0fo/+OAD3Xzzzcb/2e/SpYvOnDmj3NxctW/fXps2bVJxcbHmzJkjLy8vRUVFqbCwUB9++KFGjRplrKd169bGUaXAwEB9+eWXOnr0qFq2bKmcnBy5uLjolltukbu7uzp37qzevXtf1o/Ob3/7W0VFRaldu3Zq166dbrrpJmVlZTkckbr11luN/XZ3d9f8+fP1ww8/6LrrrjOOSLm6usrX1/ey55b4z3/+o23btmnq1KnGNtq3b68HH3xQX375pXHkrqr3qya6d++uQYMGqWvXrpLOF5IXns3l5uamuLg45eXlaeXKlRo3bpy6du2qVq1a6ezZs0pJSdHRo0f1yiuvyN/fX5JksVi0ZMkS/fa3v63xEbKq9qN8zq0WLVpIUq3m6Xj//fd13XXXGcVet27ddOTIESUlJSkiIkI+Pj7y8fFRUVFRrbeRlJSkG264QXfeeack6brrrtPOnTuVkpKiO+64Q2fPntXdd9+tXr16qWXLliouLtb//M//KDU1VQMGDNCxY8f05ZdfaubMmRowYIAk6ZprrlFSUpJOnTpl5OZK3vOafq6k82ekzZ8/XyEhIQ7r2L59u06fPq0HHnhArVq1UlRUlDIyMpSeni7p/BHdzz//XA899JAx/9e5c+f0/PPPKycnx+Ez8dVXX+nZZ5+tcCYYADRG9V1jSdKAAQOMAwn+/v6aNWuWvv76a8XExNR4HampqRo5cqTuvfdeh+VFRUX65JNP9Nvf/tY4A6V9+/b6+9//ruPHjxu/01lZWfrLX/5i/JYeOHBAGRkZkqSzZ8/qtttuU7du3eTr66tz585py5Yt+vHHH43t5OTk6JprrtGAAQNktVrVsWNHpaWlyW63y2KxVPt7W53MzEzt379ff/zjH43fqcLCQr3xxhs1zpHdbtfEiRMVExOjZs2aqWPHjvrggw+UmpqqsLAw43nXXXed7rnnHknn5x7705/+pJMnT8rHx0ctWrRQTk6OpPO1zuXeWa9du3aSpG+++cZhXrMLxcTEaPPmzRo/frysVqu+/PJLXX/99cZ7VZ39+/dr//79evLJJxUZGSlJatmypebPn68DBw4oPDzc2K6Hh4datWp12TXUgQMHtH//fiUkJCgoKEiSlJeXp8TERE2bNk02m01BQUF66KGHjLOaTp06pZ07d+rUqVO65pprtH79erVu3VoPPPCArFarIiMjlZ+fr9OnTztsq6r3oy7k5ORo2LBhRq58fX2Ns6Soj3C5aEqhyerVq5dRLEnnf3jK59nJyMhQfn6+xo0b5/Camt5Z5tSpU8rLy9N1111nLHN1ddXvf/974/G///1vBQUFycvLy1jWrVs3JSUlKSsry/gRvfCU4/JTXsvH2bdvX3300Ud66KGH1K1bN3Xt2lV9+/aVm5vbJcdmt9sr3SeLxXLJ/btwP8r/j3t2drbD8toqL77Kf9Qkyc/PT23atNG///1vo3lQ1ftVE7169dLnn3+uTz75RKmpqTpx4oRDLlq2bGn8mEqOeZHOv1+tW7c2GlLS+ffLbrcrLS3tkgXWxQ3CK92P6qSlpRmXgF44zo8++qjOtpGRkaHTp09XKIiPHTsmSfL29lZERIQ2bNigAwcOKC0tTcXFxUbTrbypc+HnJzAwUA899JDD+q4kVzX9XEnS4MGDKzSkyvendevWDt+LsLAwY/yZmZmy2+2VHvk7duyYw2fitttuo+AC0GTUZ41VrvwgkyS1bdtWLVq0UHZ29iWff3H9I50/CHTXXXdVWJ6VlaWSkhKH3ylvb+8Kv1OhoaEOjYmWLVsqKytL0vmDeD179tTWrVu1f/9+/fTTTyosLHRo5AwYMEBbt27VQw89pIiICIWFhal3795G7VHd7211yvNx4RgvzNulXJgrPz8/FRYWas2aNfrXv/6l9PR0lZWV6ezZsw6vqaxevfDs5/oWExOj999/X//6178UEhKib7/91mGKieqkpaXJarU6vD/h4eFycXHRTz/9VCeTmpc3LB955JEKsdzcXLVr105RUVH68ssvtWjRIqWmphrNvPJ8p6enq0uXLg5zWlX2Ga7r9+Pi78+wYcO0fv16paenKzQ0VD179jRudkR9hMtFUwpNVtu2bauMh4SEVDi1uKaTGlZW+Ejnf4xsNptxxKcmr69qnK1bt9aCBQv03Xff6eDBg8blf88//7yaN29e6Wv++9//1mAPLj2e8iZLXU3weKlcXRyr7v2qyrlz5zRv3jzl5+dr8ODBGjx4sM6dO6fnnnuu1uu80KX2oaysTCdPnnRYdiX7cSVjqWtDhw6tcJp1+Wfu3//+t5588kl1795dMTExuu+++/TJJ59UuT673a4ffvhBfn5+xpxPV5Krmn6uJFXakCp/3sWf88o+93/6058c5qmSVKFJealtAEBjVJ811qWcO3fOaOZUprL6p0OHDsZcPhe61G/ITz/9pJYtWxp/46vaz+PHj2vOnDnq2LGjbrzxRt1xxx3avXu3w2VvwcHBWrBggfbs2aODBw9q2bJl+uSTT/TnP//ZmK+yqt/b6pTn9ML9qUmdcPz4cePfu3bt0ksvvaR+/fpp6NChCg0N1euvv17hNc5uLPj5+alr165KSUnRqVOnZLPZjMv7asKs+slqtSo+Pr7CZ7V169aSzl9uefDgQf3qV7/SjTfeqNatWxuXPl5Kbm6uTp065VBr1PX7cfH3Z9y4cerbt6/27dun77//XklJSbrvvvs0YsQI4znUR6gp5pRCk1VV8dOxY0cdP35c7du3V6dOndSpUycdPXrUYZ6lqnh7e6tVq1YOhUf5teqfffaZpPOFyKFDh3TmzBnjOfv27ZObm5s6dOhQo3Fu3rxZ+/fv10033aSJEydq3rx5ysnJ0b59+yRJLi4uDkeyioqKtHv37hrtw4UuvJTvp59+klTxx85ms9XqjJ/g4GBZLBZjziPp/JG93Nxchx+qKylWMzMz9eOPP2rq1KkaPXq0evTo4VBw1XScx48fdzgKu2/fPlksFuMIpNVqdcj3zp07K+SkJvtR21yWj/PCXErnT0mvy1t2d+jQQSdPnjS+G506ddKuXbv07bffSjp/2Zu3t7ceeeQR3XLLLQoMDHQ4qlt+JO3gwYPGstzcXD355JPG50u6sve8pp+rqvj5+Sk3N9eYw006P/9UufLvaWlpqZGH8lPrL/fzBQCNSX3WWOUurE2OHj2qM2fOGGczX2n9ExAQIFdXV4c6rri4WE899ZS++eYbY1lV+7lz504VFRXpiSee0PDhw9WlS5cKZzh99NFHOnbsmAYOHKipU6fqD3/4g3766SdlZmZKqv73tjrltdqFlwxemLfyfbgwVz/99JPDTVe2bt2qrl27atasWRo4cKBat27tEK9JLiQZk7tfybxGzZo1q7I+iomJ0Y4dO7Rt2zbddNNNDmfAVyc4OFjnzp1zeM8PHjyosrKyOquhOnTooHPnzslqtRrvp9Vq1fr163XmzBkVFBTo66+/1rhx43THHXcoOjq6wsHNTp06KTU11SGP7733XoU5Tq+khqru+/Pf//5Xb731ljp06KDbbrtNjz32mPr372/MC0d9hMvFmVJAJYYPH64NGzbolVde0fDhw3Xq1CktX768xpMlSufnalq+fLnatGmj8PBwffXVVzpx4oRx2dDQoUP1z3/+Uy+++KL+3//7fzp8+LA+/PBDjR49usrL7y506tQpJSUlqaSkRNdee62++uorSb8chQgMDNT777+v//znP/L09NSSJUsuLxH/59NPP9W1114rb29vrVy5UiEhIerSpYvDc0JCQrRt2za1b99ep0+fltVqVc+ePatdd9u2bTVgwACtXLlSdrtd11xzjd555x21b99eN910U63GezEvLy9ZLBZ9+eWXcnFx0ffff29czlbT5k+/fv20fv16vfjii4qNjdWpU6eUmJiom2++2SHfW7duVffu3ZWWlqbVq1fX+L28UGhoqFavXq3vvvtOrq6uys7OrvHkj2PHjtUzzzyjN954Q3379tWOHTv0r3/9S0888cRlj6O6bbzzzjuKiopSamqqkpKS9Ic//EHS+dPET5w4oS+//FLu7u7auHGjUlNTjTy1a9dOffv21YoVK3Tu3Dn5+Pho3bp1atu2rcPldleiLj5X/fv313vvvaeFCxfqtttu008//aSvv/7amI+hfBt///vfVVhYqFatWunDDz/U4cOHjYnkAQCO6qLGks7PRdOhQwcFBgZqzZo1uvbaa435a660/vHw8NDIkSP10UcfqXnz5urYsaM2bdokNzc39e3bt0braNGihcrKypScnCx/f39t2bJFX375pcPlc8eOHdP27dt11113ycvLS1u2bJGrq6tx6WN1v7fVCQgIUPfu3bVy5Uqj+XTxDTsCAwP1xRdfaODAgcrLy9PixYsd5mFq0aKFDh48qG+//VYlJSVav369cnNzL7u55O3trdatW+vTTz9V//79jbsmlt8xsSZCQkKUlJSkr7/+Wi1atNChQ4cc7jp300036a233tK3336rZ5999rLGFxERoYiICC1evFgTJkyQJCUmJqpbt26XdQfkqkRGRiosLEwLFy7UnXfeqWbNmumdd97RuXPn5O3trbKyMrm5uWnXrl3y8/PTv//9b+MOf+X5vu222/T4449r4cKFuuWWW3T48GF9+eWXmjhxYp2MUar+++Pp6anPP/9cpaWluummm1RQUKAffvjBOOhIfYTLRVMKqETLli315JNPauXKlXruuefk5eWlQYMGVXrN9qUMGzZMLi4u+uijj/TRRx+pY8eO+tOf/mTMq+Pt7a2nn35ab775pp5//nm1aNFCd955p8Pdv6pz2223qbCwUG+//bZOnjyp1q1ba9q0acaPwpAhQ/TDDz/o4YcfVvPmzXXLLbeoY8eOFY6SVSc2NlaffPKJcnJyFB4eXukdU8aPH6/XXntNzzzzjFxdXY3JFWti6tSpuuaaa/T222+ruLhY119/vSZOnGgcVbtSrVu31pQpU/TBBx/o888/V+fOnXX//ffrtddeczhbpyrNmjXTk08+qeXLl+uVV16Rm5ubbrnlFofPxJQpU7R48WJNnDhR7dq104wZM4y7J16OgQMHKj09XQsWLFBxcbFiYmJq3JTq1q2bHnnkEa1evVqbN29W+/btNXfu3DorqMq3MXv2bH3wwQf6+OOP5evrq9/97nfq06ePJGnkyJHKzMzU0qVL5eLioj59+uhXv/qVvv/+e5WVlcnFxUUzZ87UO++8o8TERJ07d05hYWF64oknKr2Morau9HPl5eWlJ554Qn//+9/1/PPPq0OHDrr11lsd7u4zefJkvf3221qxYoWKi4t13XXX6YknnpCHh0ed7QcANCZ1UWNJ0pgxY7Rjxw699957CgoK0iOPPCKbzSapbuqfcePGqXnz5vroo49UWFiokJAQPfnkkzWe+6pfv3768ccf9e6776qsrExRUVEaM2aMNmzYoIKCAjVv3lz33HOPVq1apWXLlun06dNq166d/vCHPxhzAFX3e1sTM2fO1NKlS7Vw4UL5+PgoNjbW4fK78ePH69VXX9WUKVN07bXXKjY2Vp9++qkRv+uuu3TixAm9/PLL8vDw0MCBA+Xt7V3j+ulCDz74oN544w3985//1DXXXKO5c+de1usjIyP129/+Vm+88YZOnz6tyMhIh6aUl5eXrr/+eh05ckShoaGXtW6LxaKHH35YK1asMO7OfOONNyouLu6y1lOdhx56SCtWrNDixYtlsVjUvXt3xcXFyWKxyNXVVbNnz9aqVav05z//WQEBAZo8ebJeffVVHTx4UH5+fgoKCtLjjz+uxMRExcfHq3Xr1oqLi6vTO9dV9/0pv3Pm6tWrlZCQIIvFooiICE2aNMlYB/URLofFbtYFtAAanJycHM2cOVPx8fF1evkXAABAbd1xxx36wx/+UOOzlvCL8tru1Vdfvey74F3NfvzxR5WWlur111/XwIED9f/+3/9z9pAA1BBnSgEAAAAAGqyUlBRt2rRJkZGRDpNtA7j6caYUAAAAAAAATMfd9wAAAAAAAGA6mlIAAAAAAAAwHU0pAAAAAAAAmI6mFAAAAAAAAExHUwoAAAAAAACmoykFAAAAAAAA09GUAgAAAAAAgOloSgEAAAAAAMB0NKUAAAAAAABguv8PxmQ8zd72uFAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用户消费分布\n",
    "\n",
    "fig,axes = plt.subplots(1,2,figsize=(12,4))\n",
    "\n",
    "\n",
    "df['amount'].plot(kind='hist',bins=50,ax=axes[0])  #bins:区间分数，影响柱子的宽度，值越大柱子越细\n",
    "axes[0].set_xlabel('The consumption amount of each order')\n",
    "\n",
    "gp_user['product'].plot(kind='hist',bins=50,ax=axes[1])\n",
    "axes[1].set_xlabel('The purchase quantity of each user')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()  #自动调节子图间距\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "97c2df4a-84be-487a-939a-032ffd4a07da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>product</th>\n",
       "      <th>average_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16921</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>713</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9835</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>23565</th>\n",
       "      <td>7931</td>\n",
       "      <td>6497.18</td>\n",
       "      <td>514</td>\n",
       "      <td>12.640428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23566</th>\n",
       "      <td>19339</td>\n",
       "      <td>6552.70</td>\n",
       "      <td>378</td>\n",
       "      <td>17.335185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23567</th>\n",
       "      <td>7983</td>\n",
       "      <td>6973.07</td>\n",
       "      <td>536</td>\n",
       "      <td>13.009459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23568</th>\n",
       "      <td>14048</td>\n",
       "      <td>8976.33</td>\n",
       "      <td>1033</td>\n",
       "      <td>8.689574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23569</th>\n",
       "      <td>7592</td>\n",
       "      <td>13990.93</td>\n",
       "      <td>917</td>\n",
       "      <td>15.257285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23570 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id    amount  product  average_amount\n",
       "0        16921      0.00        1        0.000000\n",
       "1          713      0.00        1        0.000000\n",
       "2         9835      0.00        1        0.000000\n",
       "3         7005      0.00        1        0.000000\n",
       "4         1948      0.00        1        0.000000\n",
       "...        ...       ...      ...             ...\n",
       "23565     7931   6497.18      514       12.640428\n",
       "23566    19339   6552.70      378       17.335185\n",
       "23567     7983   6973.07      536       13.009459\n",
       "23568    14048   8976.33     1033        8.689574\n",
       "23569     7592  13990.93      917       15.257285\n",
       "\n",
       "[23570 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用户累计消费金额占比分析（用户贡献度）\n",
    "\n",
    "gp_user = gp_user.sort_values(by='amount').reset_index()\n",
    "gp_user "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e3fadad-a4fb-4dec-bed9-032b954969e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>amount</th>\n",
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       "      <th>average_amount</th>\n",
       "      <th>cum_amount</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23565</th>\n",
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       "      <td>2500315.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id    amount  product  average_amount  cum_amount\n",
       "23565     7931   6497.18      514       12.640428  2463822.60\n",
       "23566    19339   6552.70      378       17.335185  2470375.30\n",
       "23567     7983   6973.07      536       13.009459  2477348.37\n",
       "23568    14048   8976.33     1033        8.689574  2486324.70\n",
       "23569     7592  13990.93      917       15.257285  2500315.63"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#每个用户消费金额累加\n",
    "gp_user['cum_amount'] = np.cumsum(gp_user['amount'])\n",
    "gp_user.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "85873ec2-71a4-4940-8909-bb62f9914cbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(2500315.63)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#消费金额总值\n",
    "total_amount = gp_user['amount'].sum()\n",
    "total_amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e8ac3516-b8a6-4a99-beb5-66cbe3ae2070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>product</th>\n",
       "      <th>average_amount</th>\n",
       "      <th>cum_amount</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16921</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>713</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9835</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7005</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23565</th>\n",
       "      <td>7931</td>\n",
       "      <td>6497.18</td>\n",
       "      <td>514</td>\n",
       "      <td>12.640428</td>\n",
       "      <td>2463822.60</td>\n",
       "      <td>0.985405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23566</th>\n",
       "      <td>19339</td>\n",
       "      <td>6552.70</td>\n",
       "      <td>378</td>\n",
       "      <td>17.335185</td>\n",
       "      <td>2470375.30</td>\n",
       "      <td>0.988025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23567</th>\n",
       "      <td>7983</td>\n",
       "      <td>6973.07</td>\n",
       "      <td>536</td>\n",
       "      <td>13.009459</td>\n",
       "      <td>2477348.37</td>\n",
       "      <td>0.990814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23568</th>\n",
       "      <td>14048</td>\n",
       "      <td>8976.33</td>\n",
       "      <td>1033</td>\n",
       "      <td>8.689574</td>\n",
       "      <td>2486324.70</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23569</th>\n",
       "      <td>7592</td>\n",
       "      <td>13990.93</td>\n",
       "      <td>917</td>\n",
       "      <td>15.257285</td>\n",
       "      <td>2500315.63</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23570 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id    amount  product  average_amount  cum_amount      prop\n",
       "0        16921      0.00        1        0.000000        0.00  0.000000\n",
       "1          713      0.00        1        0.000000        0.00  0.000000\n",
       "2         9835      0.00        1        0.000000        0.00  0.000000\n",
       "3         7005      0.00        1        0.000000        0.00  0.000000\n",
       "4         1948      0.00        1        0.000000        0.00  0.000000\n",
       "...        ...       ...      ...             ...         ...       ...\n",
       "23565     7931   6497.18      514       12.640428  2463822.60  0.985405\n",
       "23566    19339   6552.70      378       17.335185  2470375.30  0.988025\n",
       "23567     7983   6973.07      536       13.009459  2477348.37  0.990814\n",
       "23568    14048   8976.33     1033        8.689574  2486324.70  0.994404\n",
       "23569     7592  13990.93      917       15.257285  2500315.63  1.000000\n",
       "\n",
       "[23570 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp_user['prop'] = gp_user.apply(lambda x:x['cum_amount'] / total_amount,axis=1)\n",
    "#gp_user['prop'] = gp_user['cum_amount'] / total_amount\n",
    "gp_user\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "86244c4f-25a3-4950-9ac0-0339ab0eb5df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp_user['prop'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7103fcfa-3214-4ff1-9158-7beb97f2acea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         date  count\n",
      "82 1997-01-01    209\n",
      "68 1997-01-02    241\n",
      "73 1997-01-03    228\n",
      "83 1997-01-04    174\n",
      "64 1997-01-05    250\n",
      "..        ...    ...\n",
      "79 1997-03-21    213\n",
      "65 1997-03-22    250\n",
      "49 1997-03-23    277\n",
      "57 1997-03-24    263\n",
      "69 1997-03-25    241\n",
      "\n",
      "[84 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用户消费行为\n",
    "#首购时间\n",
    "print(df.groupby(by=\"user_id\")['date'].min().value_counts().reset_index().sort_values(by=\"date\"))  #调整为按日期排序\n",
    "df.groupby(by=\"user_id\")['date'].min().value_counts().reset_index().sort_values(by=\"date\").plot(x=\"date\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e003f25-a901-4d4e-9e66-adf7b380d589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#最后一次购买时间\n",
    "df.groupby(by=\"user_id\")['date'].max().value_counts().reset_index().sort_values(by=\"date\").plot(x=\"date\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cf2590d8-cb17-4990-b427-72166f2ebb98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amount</th>\n",
       "      <th>date</th>\n",
       "      <th>product</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.77</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89.00</td>\n",
       "      <td>1997-01-12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>156.46</td>\n",
       "      <td>1998-05-28</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.50</td>\n",
       "      <td>1997-12-12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>385.61</td>\n",
       "      <td>1998-01-03</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23566</th>\n",
       "      <td>36.00</td>\n",
       "      <td>1997-03-25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23567</th>\n",
       "      <td>20.97</td>\n",
       "      <td>1997-03-25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23568</th>\n",
       "      <td>121.70</td>\n",
       "      <td>1997-04-22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23569</th>\n",
       "      <td>25.74</td>\n",
       "      <td>1997-03-25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23570</th>\n",
       "      <td>94.08</td>\n",
       "      <td>1997-03-26</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23570 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         amount       date  product\n",
       "user_id                            \n",
       "1         11.77 1997-01-01        1\n",
       "2         89.00 1997-01-12        2\n",
       "3        156.46 1998-05-28        6\n",
       "4        100.50 1997-12-12        4\n",
       "5        385.61 1998-01-03       11\n",
       "...         ...        ...      ...\n",
       "23566     36.00 1997-03-25        1\n",
       "23567     20.97 1997-03-25        1\n",
       "23568    121.70 1997-04-22        3\n",
       "23569     25.74 1997-03-25        1\n",
       "23570     94.08 1997-03-26        2\n",
       "\n",
       "[23570 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用户分层，构建RFM模型\n",
    "\n",
    "rfm = df.pivot_table(index='user_id',\n",
    "                     values=['product','amount','date'],\n",
    "                     aggfunc={\n",
    "                            'date':'max',   #最后一次购买\n",
    "                            'product':'count',  #购买产品频次\n",
    "                            'amount':'sum'  #消费总金额\n",
    "                            })\n",
    "\n",
    "rfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f790c444-9ba0-4d9b-b209-7fca3d6b3be4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>M</th>\n",
       "      <th>date</th>\n",
       "      <th>F</th>\n",
       "      <th>R</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.77</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89.00</td>\n",
       "      <td>1997-01-12</td>\n",
       "      <td>2</td>\n",
       "      <td>534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>156.46</td>\n",
       "      <td>1998-05-28</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.50</td>\n",
       "      <td>1997-12-12</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>385.61</td>\n",
       "      <td>1998-01-03</td>\n",
       "      <td>11</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              M       date   F    R\n",
       "user_id                            \n",
       "1         11.77 1997-01-01   1  545\n",
       "2         89.00 1997-01-12   2  534\n",
       "3        156.46 1998-05-28   6   33\n",
       "4        100.50 1997-12-12   4  200\n",
       "5        385.61 1998-01-03  11  178"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rfm['R'] = -(rfm['date'] - rfm['date'].max())/np.timedelta64(1,'D')  #取相差的天数，保留一位小数\n",
    "rfm['R'] = -(rfm['date'] - rfm['date'].max()).dt.days\n",
    "rfm.rename(columns={'product':'F','amount':'M'},inplace=True)\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "745156f3-80c9-4ba9-89c6-07bcffb994f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>M</th>\n",
       "      <th>date</th>\n",
       "      <th>F</th>\n",
       "      <th>R</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.77</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>545</td>\n",
       "      <td>一般发展客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89.00</td>\n",
       "      <td>1997-01-12</td>\n",
       "      <td>2</td>\n",
       "      <td>534</td>\n",
       "      <td>一般发展客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>156.46</td>\n",
       "      <td>1998-05-28</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>重要保持客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.50</td>\n",
       "      <td>1997-12-12</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>一般保持客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>385.61</td>\n",
       "      <td>1998-01-03</td>\n",
       "      <td>11</td>\n",
       "      <td>178</td>\n",
       "      <td>重要保持客户</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              M       date   F    R   label\n",
       "user_id                                    \n",
       "1         11.77 1997-01-01   1  545  一般发展客户\n",
       "2         89.00 1997-01-12   2  534  一般发展客户\n",
       "3        156.46 1998-05-28   6   33  重要保持客户\n",
       "4        100.50 1997-12-12   4  200  一般保持客户\n",
       "5        385.61 1998-01-03  11  178  重要保持客户"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rfm['R'] - rfm['R'].mean()\n",
    "\n",
    "\n",
    "def rfm_func(x):\n",
    "   level = x.apply(lambda x:'1' if x>=1 else '0')\n",
    "   label = level['R']+level['F']+level['M']\n",
    "   d={\n",
    "       '111':'重要价值客户',\n",
    "       '011':'重要保持客户',\n",
    "       '101':'重要发展客户',\n",
    "       '001':'重要挽留客户',\n",
    "       '110':'一般价值客户',\n",
    "       '010':'一般保持客户',\n",
    "       '100':'一般发展客户',\n",
    "       '000':'一般挽留客户',\n",
    "   }\n",
    "   result = d[label]   \n",
    "   return result\n",
    "\n",
    "rfm['label'] = rfm[['R','F','M']].apply(lambda x:x - x.mean()).apply(rfm_func,axis=1)\n",
    "\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6bd0dff4-aecd-4b61-855b-d732e35ae1fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zdOhQdDodMTExrFixAnBV9+3UqRNLliwB4Omnn6ZFixaMHj26zGsMHDgQo9Hotq1169YUFhZWeG6JiYls37690vfgDwlsqqwqNWgUj0GNVCIWQgjfFNsc/HtnLv89UYTdqWLQKdxaP4qhbROJNOorP0ANMhgM2O12BgwYwEcffQS4qhCvWrWKfv36Aa7A4syZM5jNZsLCwtDr9fz73/8mKytLG3mx2+3885//ZP78+dqxCwsLeeihi00/f/75Z3777TdOnDiBw+HA4XDw3HPPMWTIEADatWvHwoULadCggds5NmzYUGvvMHPmTBYuXEhCQgIzZ87UnmMymfj000/p27cvDRs2LPM+MzIyyhy31Ndff82LL77o66XzmQQ2VVZ2uNArv4/YqICKEdVQ6/c6Nr2kjo0QQnip2Obg2W+Ocjzf6pbxuHrfeXblFDPt7uurJbhxOp1YrVYMBgMGQ8VfoaqqYrVaCQu7WB7k9ttvZ/Xq1YBrKurYsWPce++9APTt25fnnnuOefPmsXr1ahwOBx988IFbc8nnn3+e559/nuPHj5Oens7PP/9cpldUeno66enpnDx5kg4dOpCens7NN9+sne/Zs2fp37+/9nODBg1YsmQJXbp0IS0tjSlTpjB37lw6duzIxx9/TJ06dTh58iSDBg1izJgxpKSkMHLkyDLv9/IprPKUBmjVSQKbKqt6wzFVVUHRUxLdnqLEPhLQCCGEj/69M7dMUAOu9Rkn8q38e2cuo2+pG/DX/emnn7j//vt92mfLli04na6VI6NHjyY6OppZs2bRvn17LBYLGzdu1KagJkyYQLt27YiPj+e5555j0KBB7Ny5E3CN1lQUTNlstjLTQxs3biQjI4OMjAy3/J2tW7cyZcoUli1b5tYd/LvvvmPr1q1s3LiRTZs2kZ6ezoABA1i6dCljx45l8ODBPr33YJDApsqqOJfrAMWgAE4iCn8krOT/ONfgMQluhBDCB/89UeRxbarz98dH3xL4123SpAnTp0/HaDS6jcSUx2q1YrPZ2LFjBx9//DGKovDll18CcOrUKZxOJ6tXr8ZoNNK+fXvsdjuzZ8/mhx9+AGDs2LHEx8czatQoALp160ZeXh4AFy5cwOFw0KRJE6KiorBardStW5dNmzZhtVpZtWoV33//PTabjcmTJ6Oqarl5SDqdzm2kpUePHnTv3h2LxUJsbCzx8fHMnj0bk8lEVlYW27ZtIzU1tdwmmYWFhXTu3Nnj9VBVlTp16nhxlf0T1MBm9uzZfP3119rPderU4e9//zvHjh3jww8/JCcnhx49evDQQw9pN2PPnj3MmDGDgoIC7r//fm1usuZVcVn2JVf80sJ8FxLTA3NaQghxlVNVFbuz4t/BDmf5X+T+SkhI8LgSqCI5OTmEh4dzww03aFM+LVu2ZOLEiUyYMIGXXnqJNWvWsHv3burXrw9ASkqK2zG+++47AF599VWOHj3K999/T2xsLEuXLnULGB599FH27dtHREQEY8aMoU+fPvTs2ZOCggLtelgsFvLz87n11lvp2rUrb731FvPmzePAgQPs2bOHw4cP07FjR8aPH0+PHj3IzMxk2bJljB8/nqNHj5KYmEjfvn2ZNGkS4KpbY7Va2bFjh8fg5eDBgyxatMjna+eroAY2hw8f5rnnnuOGG24AXJGjzWZj2rRptG3blnHjxvHJJ5+wceNGunfvTkFBAdOmTSM9PZ3OnTvz3nvv0ahRI1JTU4Nw9lWcJ3S676qgEl6wXXJshBDCS4qiYNBVHLDodUpIrZI6d+4cN9xwA2+++aZbPswtt9yCwWBg8eLFzJkzp9Il0j/99BPffPMNs2fPZvDgwYwbN45Ro0bx2WefaaMokyZNIikpiaefflrbb968ecydO5dnn30WnU6nTUWtXLlSe86+ffsAGDZsGJMmTWLnzp3aNNilevbsyaBBg9xWc2VmZhIfH1/hiEyzZs2YMGFCJVfKf0ELbBwOB8ePH6dVq1Zu837//e9/KS4uZsSIEZhMJoYMGcKsWbPo3r07W7ZsIS4ujgceeABFURg4cCAbNmwIUmBTxaS0cuIhRbVw3bEPONdwjAQ3QgjhhVvrR7F63/lya57qfn88lBw8eJDevXvz5z//mf3797s9NmXKFPr27UvXrl256667PB5j9+7dPPbYY0yfPp3IyEgABg8ezI4dO7jnnnuYPHkyd9xxR5mRHoDY2FiWL19OVFQUf/3rX8s9fmmRv7y8PHJycsjKyirznHnz5rFjxw769Onjtv2LL77gzjvvrPgi1JCgBTbHjh1DVVWefvpp8vLyaNWqFY888ghHjx6lRYsWmEyuL/jrr7+eEydOAHD06FFat26tReHNmjVj4cKFHl/DZrO5RcaKomiFiPyP5KvYK6qcOjYKoLefIersGoqS7vXzvMSlSu9zKP3lJi6S+xP6QvUeDW2byK6cYk7kW92CGx1Qv3YYQ9smBuvUyiidomnVqhUdO3YE0HJubDYbH3zwAa1bt2bXrl2sW7eOnj17uu3vcDg4c+YMzz33HK+//jq33Xab9h0K8Pbbb/P222/z0Ucfcdttt+FwOPjxxx85dOgQaWlpAERGRvKPf/wDu93OoUOH2Llzp8dVTJXVynE43L//tm/fzrJly1izJjD9Dv39rAUtsDlx4gT16tXjz3/+M9HR0cydO5ePP/6YBg0akJh48QOpKAo6nY4LFy5QXFyszT2Cq2RzaSJVeZYuXaoVGgJo3Lgx06ZNczt+1eUG4BgXKahEluwn5pJS2yJw6tYN/OoIEThyf0Jfddwjs9lcZhWPt2objbzTrxnzdpxi2/ECrY5NWoMYhneoE1J1bBYvXkzbtm2JjY3lt99+4//9v/+HXq8nIyODqVOncvPNN/P111+zadMmHn/8cVq1asUnn3xC7dqu/oJWq5XatWuzadMmt4Rlq9WqXb//9//+n7bd4XDw8ssvU69ePXr16qU957bbbgPgxRdfZOnSpTz55JPlXv/SwKW8EZjz58/Trl07bT9VVXnllVcYMmQIbdq08ftahYWFubWcqApFDUaHqnKcOXOGMWPGcM8996AoCiNGjNAe+8tf/sJrr73G3LlzueGGG7QhMKfTydChQ/n000/LPaanEZvc3Fzsdrtf57t80VkebpHlUwcEFRVH1KceF1Q59DHkNX5e2ioEkKIo1K1bl5ycnKA0YxMVk/sT+qrzHuXn5xMTExOQY4Vy5eFff/2VkpISbr75ZhwOBytXrqR79+6sWbOGhIQErSgeuGrMfPvttwwcONDr419ex8ZfqqpSXFxc7sqn8lgsFux2u9fPr0hBQYEW0F3KYDB4PSgRMsu9Y2JiUFWV2NhYjh8/7vaY2WzGYDBQq1YtCgoKymz3xGg0evxrwP9/oJaq7VbRvztF51prJb/gA650qaMITXJ/Ql+o36NQDWrA1WqglF6v1/oulRe8xMfH+xTUVAdFUXwKUkwmk5Y+Egj+fs6C1t17/vz52tI1gP3796MoCg0bNnRLrDp9+jQ2m41atWrRtGlTt3bvR44cIS4urkbP+6KqDZ966u4tvaKEEEII/wUtsLn++utZtGgRu3fvZufOncyYMYOuXbvStm1bzGYz3377LQBffvklbdq0QafTccstt7B371527dqF3W5n+fLltG3bNkjvoIoRpfSKEkIIIapN0Kai7rjjDk6cOMHbb7+NTqejS5cuDBkyBL1ez6OPPsr777/PggULUBSFiRMnAq7pqhEjRjB16lTCw8OJioriscceC9I78DD04gPVCc6w66RXlBBCCBEgQc2x+Z//+R/+53/+p8z2W265hb///e8cPnyY5s2bEx0drT3Wq1cv2rVrR1ZWFi1btnSrgVOzqjjYdelybwVXdFPV0R8hhBBCuAmZ5OHLxcbG0qFDh3IfS0pKIikpqYbP6HL+z+IpCugd+UTk/0BY8SHpGSWEEEL4KWg5Nle+wK2KurRnlBBCCCGqTgKbKqviqigPKxJLe0YpzioGTEIIIULe448/7ra6tyouLYly/vx5P8+oYhaLhczMTJ/3y8/P57333gtKiQAJbKrMjyaYHpT2jJLgRgghrj5ms5nVq1e7VdZds2YNN9xwAz169KB169bMmTOnzH5fffUVs2bNAlxBTadOncjKyuLHH3/knnvuITe3bCX8uXPn0qRJE1JTUz3+17RpU954440Kz/nEiROkp6f7HNxERESwYMECvvnmG5/2C4SQzbEJfVWs8mgBIsp/6NKeURcS090fVFWpSCyEEFeohQsXMnv2bAAGDRoEwKJFi9DpdNxxxx3MmDGDZ599ttwFMevXr6dBgwYA/PLLL7Rp04aUlBRSUlLo2rUrw4cP58svv3QrWGs0GhkwYABvvfWWx3MqXZVcas6cObzwwgtlekjpdDqtqGAph8PB9OnTeeCBB8o9dlhYWKWdyquLBDZVVsUgo5JFXAoqpqLfuJCYjuK0EHX2G0xFv7lWTym635eG3y1JxkIIcYlQbqkAro7Z/fv35+GHHyYsLIy0tDQUReHcuXPEx8drz7v8PZjNZjZu3MiqVasAV4fv22+/XXv8lVde4fvvv8dgMOB0OrVARafT8c033/Drr78CrmK3CQkJ6HQ6cnNziYuLo6SkhCFDhmjHMhqNdOrUSeux+PTTT9OiRQtGjx5d5v0MHDiwTGX/1q1bU1hYWOF1SExMZPv27ZVeL39IYFPTnEBlvdlUB4qjhOtOfIjelotyyXJwWUElhBAudpvK3t1mcrJs2qB23RQjN7aJwGAMrSDHYDBgt9sZMGAAH330EeBqr7Bq1Sr69esHuAKLM2fOYDabCQsLQ6/X88knn5Cdnc1tt92G0WjEbDYTHh7OwoUL3Y5vtVqZOnUqDz74IOAKbO6++25txKZz587Mnz+fpKQk7rjjDmbNmqWNApVq2LCh1rdq5syZLFy4kISEBGbOnKk9x2Qy8emnn9K3b18aNmxY5n1mZGSUOW6pr7/+mhdffLEKV883EthUWRX/0Xizm6IjKm9NmaDGtfvFFVRlpquEEOIaYbepfLeukMIC98TFIwesnDll5w89o6sluHE6nVitVgwGQ4W9CsE1imS1Wt06ct9+++2sXr0acE1FHTt2jHvvvReAvn378txzzzFv3jxWr15NfHw8Q4cO5YEHHiApKYmTJ0/SvXt3MjMz3Xo5ldcEs3Q6qU+fPpw5c4ZTp07Rp08f9Ho9OTk59O/fH6fTyauvvkp6uuu7pEuXLqSlpTFlyhTmzp1Lx44d+fjjj6lTpw4nT55k0KBBjBkzhpSUFEaOHFnm/V4+hVWeS6e+qosENlVW+Q2sChWwRLXCVLSnTFBT6tLpKiGEuBbt3W0uE9SUKixwsne3mdQOkQF/3Z9++on777/fp322bNmC0+k619GjRxMdHc2sWbNo3749FouFjRs3alNQEyZMoF27dtr0VO3atbVu1/Pnz6dbt25eNagsDSBKg6jOnTuzdOlSbcTm3//+d7kjK9999x1bt25l48aNbNq0ifT0dAYMGMDSpUsZO3YsgwcP9um9B4MENlVmr9puFfwBoQIOYx2K4u7CdOGXio+jOiShWAhxzcrJqngBR85JG6nl13j1S5MmTZg+fTpGo9FtJKY8VqsVm83Gjh07+Pjjj1EUhS+//BKAU6dO4XQ6Wb16NUajkfbt22O325k9ezY//PBDmWNlZ2cza9YsdDodaWlp2naLxYKqqmRkZFCrVi231/aG0+nE6XRqo089evSge/fuWCwWYmNjiY+PZ/bs2ZhMJrKysti2bRupqanlBleFhYV07tzZ42upqkqdOnW8Oi9/SGBTZQFoqXDZZntYXc7Xf9SVO6NUcnxFJ0GNEOKapKoqlZVHUZ3Vk1CckJDgcSVQRXJycggPD+eGG27QpnxatmzJxIkTmTBhAi+99BJr1qxh9+7d1K9f321fVVV55plnuP766yksLOSbb74hNjYWgKeeeorU1FS3oAZcAc/y5cvZsmULeXl5hIeH07t3b86fP09iYiIDBw4EXIHNyJEjeeyxx5g3bx4HDhxgz549HD58mI4dOzJ+/Hh69OhBZmYmy5YtY/z48Rw9epTExET69u3LpEmTAFfdGqvVyo4dOzwGLwcPHmTRokU+XztfSWBTZYErOuQ6kp78eiO1hGBLVEsi8n8odzpKRcES1Spgry+EEFcSRVEq/btOUcquMAqmc+fOccMNN/Dmm2+65cPccsstGAwGFi9ezJw5c8pdIv3mm2/y888/s2rVKpYuXcr48eOZMWMG69at47fffit3SXdWVhajRo3i2Wef5YknnqB79+706tWLP/zhD6xbt47IyEjGjRvHY489RqtWru+Tffv2ATBs2DAmTZrEzp072blzZ5lj9+zZk0GDBrmt5srMzCQ+Pr7CEZlmzZoxYcIE7y9aFUlgU9PKiYcUQMVJ5LlNWt5MUfzdhBUfKpNArKLgMCZRFN+rhk5YCCFCT90UI0cOeJ5uqZtSxerw1eTgwYP07t2bP//5z+zfv9/tsSlTptC3b1+6du3KXXfdpW13OBy88cYbfPrpp8yfP5+GDRsyduxYRo4cyahRozh48CBLlizBZDKVSR7+9ddfGTBggNu2iIgIBg4cyEsvvURUVBR2u53mzZtrj7/22muAa2l6Tk4OWVlZZd7HvHnz2LFjB3369HHb/sUXX3DnnXdW7eIEmFQerrIqJg97GOgpTQjWnqYzca7BY5hrd8JhuA6HPgaH4TrMtTtxrsFfZKm3EOKadmObCKJjyv8Ki47RcWMbD5VQg6B0iqZVq1b079+fRx99FKvViqIo2Gw2PvjgA1q3bs2uXbtYt26dtt+PP/7Ipk2bWLVqFe3atQPgyJEjhIWFsXnzZq677jpOnz5d5vWKi4v56aeftFwcp9PJsWPH+N///V/GjBnD4cOHWbp0KX/729/K1KIBKm2D4HA43H7evn07y5Yt45FHHvH10lQLGbGpssAnD1+eEKzqTFxITHeN4kiisBBCaAxGhT/0jHbVsTlpK61hSt16oVfH5osvvuCmm24iKiqKvXv38uyzz6LT6di8eTPTpk2jQ4cOrFixgs2bNzNu3DhatmzJzJkzue222/jqq6/Ys2cPU6dO5fvvvyc7O5vRo0czffp0FixYwIgRI4iOjubWW2+lf//+dO7cmWXLltG0aVNSUlIA1+jNihUrGDduHJGRkSxatIgpU6Zw11130a1bNwYNGkTPnj218y0pKQFcicSXy8/P56abbtJ+VlWViRMnMnjwYG688cZqvpLeUdRgdKgKstzc3DLDdr5a8dkZHm6R7VOsoaLiiPjU42CPwxDL2UbP+nVewp2iKCQnJ5OdnR2UZmyiYnJ/Ql913qOCggJiYmICcqxQrjz866+/UlJSws0334zD4WDlypV0796dNWvWkJCQoBXFAzh79izffvutltwLsGvXLj777DP69etHWlqaWy0Ym81GZmYm69atY+jQoVx//fX88ssvnD17lq5duwLwwQcf0KlTJzp0cF8mlpWVxeLFi+nVqxetW7fWtquqSnFxsVfLysGVqGy3271+fkU8fSaMRiOJiYleHUMCmypa8VkeD7fI8j2wifrU46ooc0waF5L6+3Vewp18cYY2uT+h70oJbK5l5RXou1IFIrCRHJsqSh8cV7UdHZU/RQghhBBVI4GNH85bdZXWUijDwzSUApiK95f/oBBCCCG8IoGNH1Ycq4NTxbfgxpvkYSGEEEJUiQQ2frjrwUT+fcjH8tAVTUVJNWEhhBDCLxLY+Cl9+PW+7eDhiquAJfIGv89HCCGEuJZJYOOnksJC33aoKHFdlczikCDTgUKIavL4449z4MABv47hdDo5e/Zshc8pKSnh5MmTZGRk8Oqrr7J48WLsdrtWo8ZbFouFzMxMn88xPz+f9957LyirHSWw8VNhUZFvO1SQPBx+YZff5yOqRnFaqJW7nPj/m0b8/71O/P9No1buchSnJdinJoS4SpjNZlavXk1ycrK2bc2aNdxwww306NGD1q1bM2fOHLd9nE4nKSkpmM1mbdvKlSsZMWIEFovn30+33347DzzwAP/4xz+w2+0oisI333xDu3btaNasGa1atSItLY1mzZrxxhtveDzOiRMnSE9P9zm4iYiIYMGCBXzzzTc+7RcIUnnYT/EJCXD4mPc7OPEc3Kg2qTAcBIrTwnXH/1mmL1dE/g+EFR/iXIPHpIWFEMIvCxcuZPbs2QAMGjQIgEWLFqHT6bjjjjuYMWMGzz77LOHh4W776XQ6TCYTERGuFhH5+flMmjQJi8XCHXfcAUCtWrVYsWIFkZGR2n4Gg4GPPvqI1NRUli9fzv333w9A3759+eMf/8iDDz7IgAEDGD58OA0aNABgzpw5vPDCC+j1+jLncN9997ltczgcTJ8+3WOn87CwsHIbetYECWz8pDqdvu1QYYspmQIJhqiz35QJasDVv0tvyyXq7BqtOakQIjSFcuVhcDWW7N+/Pw8//DBhYWGkpaWhKArnzp1z65J96XvYsWMHBw8exOl08tlnn9GmTRumTZvG448/zogRIwB45plniImJcQtqLj1OYWEhkydPJioqil69elFYWMjPP/+sjdIUFhZqrReMRiOdOnViyZIlADz99NO0aNGC0aNHl3k/AwcOLNNnqnXr1hRWkp6RmJjI9u3bvbpmVSWBjZ8ifC0hXeHkn6yKCgZT0W9lgppSpc1JJbARIvRYrVa+//57Dh8+jNPpRKfT0aRJEzp16kRYWFiwT8+NwWDAbrczYMAAPvroIwD0ej2rVq2iX79+gCuwOHPmDGazmbCwMH777TfWrl2Lw+Hgq6++Ijc3l7i4OIYPHw7AuXPnWL16NZs3by7zeqX5NJ9//jkOh4MXXniBLl268Pbbb3PbbbfRsGFDzGYzx48fp04d1+rehg0bau0dZs6cycKFC0lISGDmzJnacU0mE59++il9+/alYcOGZV43IyNDGwG63Ndff82LL75Y9YvoJcmx8VORr8nDlQzwSE5HDVNVUCu5KVJfSIiQY7VaWbx4MTt37qSwsJCioiIKCwvZtWsXixcvxmq1VsvrOp1OSkpKsNsrb4SsqioWi8Utgfb2229n9erVgGsq6tixY9x7772Aa5po8eLFdOvWjfPnzzN06FDmzJmD0Whkzpw5PP7445w9e5b27duTlpbGzTffjMFgoE+fPjRp0oSMjAwATp8+TW5uLiNHjuTo0aOsWrWKV155hbfeeovly5fz+uuvs2bNGpo3b47JZKJJkyYAdOnShYcffpgpU6bw5ptv0rFjR9asWcO2bdtYunQpBoOBMWPGkJKSwsiRI7WO46Uun8Iqz6V9rqqLjNjUNCcVhJNOrjv2AecajpGcjpqiKK76QRU+R0bShAg133//PXl5eWW2q6rKuXPn+P7777UmkIH0008/afkq3tqyZQvO39MWRo8eTXR0NLNmzaJ9+/ZYLBY2btyoTR1NmDCBdu3auU1PAcyYMYNGjRoRFhbGP//5Tzp16sQtt9zC8uXLadSoEffdd582NZSQkMBf//pXHnzwQW1UJSUlhbp16zJkyBDq1q1LYmIiS5cupX379hgMF0OB7777jq1bt7Jx40Y2bdpEeno6AwYMYOnSpYwdO5bBgwdX+drVFAls/FSa0BUICqC3S05HTbNEtSQi/4dyp6NUFCxRrYJwVkKIihw+fNjjY6qqcvjw4WoJbJo0acL06dMxGo2VTndZrVZsNhs7duzg448/RlEUvvzySwBOnTqF0+lk9erVGI1G2rdvj91uZ/bs2fzwww+AK8iYNWsWFouFuLg4unfvzvz583nssccwGAzk5OQwYMAAFEXh9OnTWiNMnU5Hr1696Ny5M/Xq1dPO5+zZs4SHh2tduIuKimjVqhWLFy/WntOjRw+6d++OxWIhNjaW+Ph4Zs+ejclkIisri23btpGamlpuJ+/CwkI6d+7s8XqoqqpNe1UnCWz8dKGwkFoBPJ4CmC7skcCmBhXF301Y8aEyCcQqCg5jEkXxvYJ4dkKIy6mqqo2AeOJ0OqsloTghIcHjSqCK5OTkEB4ezg033ECXLl1IS0ujZcuWTJw4kQkTJvDSSy+xZs0adu/eTf369QHXSE/37t3ZtGmT9prz5s0D4J133uHgwYNs376d7du3l+nubTAYqF27Ntu2bdO2/eUvf6Fbt27aqMtnn33GypUrtcfnzZvHgQMH2LNnD4cPH6Zjx46MHz+eHj16kJmZybJlyxg/fjxHjx4lMTGRvn37MmnSJMC1WstqtbJjxw6PwcvBgwdZtGiRz9fOVxLY+CnByzbqGi+uuOIokmXfNUjVmTjX4DGizq7BVPSbK6dG0WOJaklRfC+ZFhQixCiKUmmuhk6nC6lVUufOneOGG27gzTffdAtCbrnlFgwGA4sXL2bOnDluS6Sff/55ACZOnOh2rPPnzzNv3jzefPNNjyuMdDod+fn5pKWladvOnj3Lli1beOeddwDXiE2HDh20x/ft2wfAsGHDmDRpEjt37mTnzp1ljt2zZ08GDRrkNl2WmZlJfHx8hSMyzZo1Y8KECR4fDxQJbPx0/MQJGvuyg4NKlnyDgl2Cmhqm6kxcSEx3jZRJUClEyGvSpAm7du0qt7KtoihaQmyoOHjwIL179+bPf/4z+/fvd3tsypQp9O3bl65du3LXXXdVeBybzca4ceP4wx/+QNu2bbXtqqpy4sQJtxVJ3ozYrFq1Snv8tddeA1xL03NycsjKyirz+vPmzWPHjh306dPHbfsXX3zBnXfeWdllqBES2PjJmyxwN14ENqpikC/XYJLrLkTI69SpE8ePH+fcuXNuwY2iKFx33XV06tQpiGfnrnSKplWrVnTs2BFAy7mx2Wx88MEHtG7dml27drFu3Tp69uyp7Wu327Vpt1OnTjF27FiKi4uZPn06VquVoqIiVFVl37599OvXj19++YXw8HCcTqdXIzbt27cvc76VtUFwONzb/2zfvp1ly5axZs2aql2gAJPAxk9169WDPfsrf2IpY+VPUVQHimpFVWQKRAghyhMWFsaDDz54RdSx+eKLL7jpppuIiopi7969PPvss+h0OjZv3sy0adPo0KEDK1asYPPmzYwbN46WLVsyc+ZMSkpK6NKlC126dOHs2bP06tWLrl27MnXqVKKionA4HCQnJ9OhQweKiooYPny4VrnYarWWGbG53GeffaYV47tUaT+pHj16lHksPz+fm266SftZVVUmTpzI4MGDufHGG/29VAGhqMHoUBVkubm5ZRKtqkJRFExGI9dt2+7VH/kqKo6ITysfsQEcxiQp5R8AiqKQnJxMdnZ2UJqxiYrJ/Ql91XmPCgoKiImJCcixQrny8K+//kpJSQk333wzDoeDlStX0r17d9asWUNCQoJWFA9coyrffvstAwcOBFyBRO3atQHIysrSqgRfymg0BuQ7rZSqqhQXF5e78qk8FosFu93u9fMr4ukzYTQaSfQyp1UCGz8oikK4Xk/sj5neBzaRn3pVFlFFwVy7k6yO8pN8cYY2uT+h70oJbK5lgQ5sgikQgY1UHvZT+GX9OSrl5e+F0lL+QgghhPCeBDZ+KszP920HX0ZKpZS/EEII4RMJbPxUKzratx18ueJSyl8IIYTwiQQ2fiosLvZth0r6LZaSUv5CCCGE7ySw8VOUIfAr5ktXRUkpfyGEEMI3Etj4Se9r8nAFVMChq4259u2ca/AXWeothBBXELvdzvHjx/n222/55JNPtO0OhwOz2ezVqjK73Y7ZbC6z/ZVXXuHgwYPaz7t27Sq3Bs3lzp075/az2Wwu9/ieWCwWMjMzvX5+qfz8fN57772grHaUAn1+KsnPJ9yXHSoJJVW9SfoTCSFECFNVlSeeeILs7GxKSkooKCggPz8fm81GSkoKiYmJJCQksH//flq0aMFvv/3GI488QkREBIqiYLVaOXr0KM2bN8disZCVlaW1gLDb7TRs2JC5c+dqr3fkyBHmzp3LX/7yF23bjBkzyMjIoF+/fhiN5Vd+zc7OplOnTqxcuZLU1FQAli5dyvvvv88PP/zgVd2fEydOkJ6ezooVK8qtUuxJREQECxYs4MYbb6R3795e7xcIEtj4KayWj729bXisPqwAelsuUWfXSP0aIYQIUYqiMG7cOJxOJ3FxcdSuXZvx48dz44038uijj5Z5fmpqKhkZGdrPBw8eZOjQoaxdu5a9e/cyduxY1q5d6/H1Fi5cyH333UdSUhLgala5efNmevfuzaRJk3jjjTfK3W/p0qWkpaWxfv16Ro4ciU6no6ioiOLiYm677TbAFUi9/vrr3HXXXcyZM4cXXnihTKsgnU7Hfffd57bN4XAwffp0j53Ow8LC3Bp61iQJbPxkMvk4slJpA0yV8ILtMmojhBC+qKH+erm5uXTt2pWoqCi3DuN5eXmsXbvWbQrK6XRy4cIFtm/fTmFhIffeey/gCiZyc3NJS0vDbrdz9uxZradT06ZNWbhwoXaM8+fPs3DhQt59913A1e7gqaee4rXXXqN37948+OCDTJ48mWeeecZtBKa4uJh//etfvPnmm3Tr1o3HH38cvV7Pe++9x759+/jwww8BV+uF0kDGaDTSqVMnbYrr6aefpkWLFowePbrMdRg4cGCZkaLWrVtTWFhY4fVLTEz02JE8UCSw8VNxSQk+Lfh2UOl0lKJauO7YB5xrOEaCGyGE8EBxWog6+42rmKnqBEWHJaolRfF3V9vvzsTERPbs2VNm+xNPPOFxxAZcDSfz8vI4cOCANmKzbds2txGbrVu3MmnSJLf93n33Xc6fP4/RaKS4uJiHH36Y1NRU+vXrB8Ds2bP5n//5HzIzM5k0aRLNmzcH4B//+Ae5ubkkJSVhNBp58skn2bt3L9nZ2TgcDu655x4AXn31Va0xZ8OGDbX2DjNnzmThwoUkJCQwc+ZM7XxMJhOffvopffv2pWHDhmXeZ0ZGhluH8Ut9/fXXvPjiixVd3oCQwMZP4R7mNj3y4oorgN5+RqakhBDCA8Vp4brj/0Rvy0W5pKR7RP4PhBUfqvZeez/99BNDhgzRfrZareh0Ot5++21t2/Lly2nZsmWVX2Pz5s189tlntGvXDpvNxr333kunTp245557KC4uJjIykry8PF588UUWLlzI66+/zqxZs/j555/517/+RWxsrHaso0eP8swzz9C9e3dt29ChQ7FYLNrPXbp0IS0tjSlTpjB37lw6duzIxx9/TJ06dTh58iSDBg1izJgxpKSkMHLkyDLne/kUVnkuHeWqLhLY+En1dehTxavqw6UtFSSwEUKIsqLOflMmqAHX786ayFXU6/UkJydr0zYvvfQSzZo1Y/jw4QD06dMHwyXlQFRVpUGDBlrHbIPBQKdOnYiIiAAudtJu3Lixts/x48d5+umnycjIwGg08t5775Gamkq7du34z3/+Q6NGjdi4cSOZmZn8/e9/x+FwAPDll18yevRovvnmG+1YOp2O8ePHa68HcOrUKcaMGeP2vr777ju2bt3Kxo0b2bRpE+np6QwYMIClS5cyduxYBg8eHLBrWF0ksPFTicVCWHUdvLSlglQfFkIIN6ai38oENaVq4g9DRVE4duwY6emu18jLy2PLli18/vnnAOTk5Gg5LxaLhfj4eDZs2KDt/9RTT2E2m7Vcl1JOp5P8/HwiIiIYOnQogJZ4XLqyyWazUadOHcAVIJXmupSOmLzwwgvodDq3wAbg7bffLjNic7kePXrQvXt3LBYLsbGxxMfHM3v2bEwmE1lZWWzbto3U1NRyO3kXFhbSuXNnj9dMVVXtvKuTBDZ+Mnkx9ObGlxhFWioIIURZqurKqanwOdX7h6HNZiM2NpZhw4YBsHLlShITE7Uk4HfffRe73Q7Aiy++yPr167URnLy8PFRVJT4+nrS0NC5cuEBJSQkJCQmoqorVamXGjBla7kt5Lh15uZynRS3PPfecW0By4sQJt8fnzZvHgQMH2LNnD4cPH6Zjx46MHz+eHj16kJmZybJlyxg/fjxHjx4lMTGRvn37ajlB+fn5WK1WduzY4TF4OXjwIIsWLfJ43oESMoHNa6+9RufOnenWrRt79uxhxowZFBQUcP/992tJUgA//PAD8+bNw+FwMGzYMP7whz8E8azBardTHbO4ZVoqyMiNEEK4KIrrD78Kn1O9fxg2bdqUyZMnaz/XqlWLuLg4GjVqBMD7779PcnIygNty7B9//JHRo0ezYMECUlNTsVqt9O/fn1GjRnlcOn2po0ePasu+ffX6669XOGKzb98+AIYNG8akSZPYuXMnO3fuLHOcnj17MmjQIOLj47VtmZmZxMfHVzgi06xZMyZMmFClc/dFSAQ2W7ZsYefOnXTu3JmCggKmTZtGeno6nTt35r333qNRo0akpqZy7Ngxpk+fzqhRo2jevDlvvfUWTZo0oV69ekE7d8XXRCgHZa765TGLioLDmETxdV2plbu8RjP+hRDiSmCJaklE/g/lTkfVRK+94cOHU1JSQni4q0Tr0aNHOXbsGIcOHXKdn8XCW2+9xcaNGy+el6qydu1a6tSpw4gRI7TRmiZNmngV1AD8+9//5vbbb6/SOVc2YvPaa68BrhGlnJwcsrKyyhxj3rx57Nixgz59+rht/+KLL7jzzjurdF6BFvTA5sKFC8ybN08LTrZs2UJcXBwPPPAAiqIwcOBANmzYQGpqKhs2bKB169baxevduzebN2/mj3/8Y7nHttls2Gw27WdFUbThO28qLlZGURQifG2pUG4cpOJQarseU/RYolpijutG7ImZ6G2n3WavIvK/J6z4EOdlKbhXSu9zIO63CDy5P6EvVO9RUfzdhBUfKpNAXPqHYXX32lu5cqXbz5Ut9wbXNfzb3/7G2LFjWblyJZMnT+amm24iIyOD/v3706NHD/r06UOzZs3c9rNYLKiqyldffcX8+fP5+uuvtcecTqfHtgUOhwOr1apNiZU3YqOqKhaLxW36qrI2CKVJyqW2b9/OsmXLWLNmTYX7ecvfz1rQA5t58+Zx6623YrVaAVfU27p1a+2NNWvWTCtWdPToUdq1a6ft26xZswp7ZSxdutTt8caNGzNt2jQSExMDdv4XCgoom0LlO31ENLq2E9AZIzEBkfvngO10mecpgMF2moSijRha/CkAr3xtqFu3brBPQVRA7k/oq457ZDabPbYDqJyRwsZ/JSL3a4wX9qCoDlRFj61WK8yJvTHofWp245P27dtjNBq10RqAs2fPsnXrVi15GFx/XF+4cIFt27bx//7f/+PMmTMcO3aM/Px8evfuzZdffklqaioOh4Pvv/+ezz77jN69ezN8+HC3ejZ2ux2r1cqsWbOYPn26W+BTGryUdx1tNhsZGRk88sgjGI1Gnn/++TLPefLJJ2nQoAHLly93OyZQ7gjM+fPnadeunfZ6qqryyiuvMGTIENq0aePLZSxXWFiYNoVXVYoajA5Vv/vll1/44IMPeOedd5g9ezatW7fmp59+okWLFlqFxpKSEh555BHmzp3Ls88+S//+/enUqRPgWgo3ffp03nzzzXKP72nEJjc3V4tg/aEoCnHXXYdx3UavpnJVVBy1PvXwGJhr305Rkut9xx98GZ1qKfe5AE7FxNlmr1TltK8piqJQt25dcnJygtKMTVRM7k/oq857lJ+fT0xMTGAOFuJ5iBs2bMBsNtO8eXOaNWvmsZ5Lfn4+RqORyHJmA1RVLXc0w2g0un3X+UtVVYqLi8td+VQei8WC3W73+vkVKSgooHbt2mW2GwwGrwclgjZiU5r1PXr0aLfsbr1e77b2PywsTBvN0ev1blGp0Wh0Ky50OaPR6PGvgUD9A7VZLJ5aP5XPQx0bBYjI34qpaA+WyBtRVGuFh1FUK6rTGdL/kEOJqqryxRnC5P6EvpC/RyH+u7C0Tk1lyvtSL1VT04GKovgUpJhMJt/bC1XA389Z9ZcA9OCLL76gadOmdOjQwW17rVq1KCgo0H42m81aoHP5YyUlJW5BUFBUJXnYA1fF4fNEFPwAHuozCCGEEMKzoEUF3333HQUFBfzpT38CXENZ33//PQA33HCD9rwjR44QFxcHuJbX7d+/X4t8L30sWMIrqCVQVQqVhzWqYgz5v1CEEEKImha0wObVV191y6yeP38+zZs3p1u3bvzlL39h165dtGrViuXLl9O2bVsA0tLSePHFF+nTpw9JSUl89dVXdOnSJVhvAQBLcTE+hTZeXvHS4Ka80EUFSmrd5MurCiGEENeEoAU2lxb2AQgPDycmJoaYmBhGjBjB1KlTCQ8PJyoqisceewyARo0a0adPH5577jmMRiPJycncfffdwTh9TXVOhVU4HqP4WPFYCCGEuAYEfbl3qUsbcfXq1Yt27dqRlZVFy5Yt3ZbUDRkyhC5dupCXl0erVq2CnmOj+Pr6TvzObFIAU/F+Lvh3GCGEEOKqE7Tk4cokJSXRvn17t6CmVP369bnpppuCHtQA2ootr5O4A3XFS/ugCCGECAl2u53jx4/z7bff8sknn2jbHQ4HZrPZq9U+drsds9lcZvu8efM4f/58IE/XKxaLhczMTJ/3y8/P57333gvKSrrgRwZXuNIlbl7n8XpKnPGVNMgUQoigUFWVJ554guzsbEpKSigoKCA/Px+bzUZKSgqJiYkkJCSwf/9+WrRowW+//cYjjzxCREQEiqJgtVo5evQozZs3x2KxkJWVRZMmTQBXYNOwYUPmzp2rvd7x48eZPHky99xzD1OnTmX+/PlER0drjxcWFjJ06FBeeOEFt/N8/vnnad68OX/+85/dtrdu3Zrly5fTtGnTSt/riRMnSE9PZ8WKFbRv397raxQREcGCBQu48cYb6d27t9f7BYIENn6ylZRQ09kuNdEHRQghRPkURWHcuHE4nU7i4uKoXbs248eP99hSITU1lYyMDO3ngwcPMnToUNauXcvevXsZO3Ysa9eu9fh6S5Ys4Y9//COJiYkYjUYee+wxHn/8ce3xDz/8kKKiojL7FRcXa4UAS2cXwsLCMBgMGI1GnE4nFotFqyU3Z84cXnjhBfR69281nU7Hfffd57bN4XAwffp0jz2uwsLCePLJJz2+p+okgY2f9FUuB1650gE8xW1bzfRBEUKIK4mnqryBlpubS9euXYmKinKrHpyXl8fatWvdpqCcTicXLlxg+/btFBYWahX17XY7ubm5pKWlYbfbOXv2LGlpaYCrrElpGyFwVdD/9NNPeeedd7T3WZnjx49TWFhIYWEhubm57Nq1i19//ZWPP/4YnU7H+fPnGTp0KDqdjpiYGFasWAG4itp26tRJa0X09NNP06JFC0aPHl3mNQYOHFimAG7r1q0pLCys8NwSExPZvn17pe/BHxLY+Emn93G8xoepKBUjJTEdMBXvd+XU/N4gsyi+lzTAFEJc82wOM7tPf05WYSaq6kBR9KREt6dN0iCM+sDXGAPXF/OePXvKbK+sCWZRURF5eXkcOHBAG7HZtm2b24jN1q1b3XpEgaubd1ZWlhZE2e12/vnPfzJ//nztOYWFhTz00EPazz///DO//fYbJ06cwOFw4HA4eO655xgyZAgA7dq1Y+HChTRo0MDttRo2bEi3bt0AmDlzJgsXLiQhIYGZM2dqzzGZTHz66af07duXhg0blnmfGRkZZY5b6uuvv+bFF18s97FAksDGTyUlJb41wfQ6qAFHWDwXkvq7Vj+FeB8UIYSoSTaHmXVHXqHAcpJLS5oeyFvPqaI99Gz8crUFNwA//fSTFiiAa6pHp9Px9ttva9uWL19Oy5Ytq/wap0+f5oMPPuDGG2/Utj3//PM8//zzHD9+nPT0dH7++ecyvaLS09NJT0/n5MmTdOjQgfT0dG6++WZtwc3Zs2fp37+/9nODBg1YsmQJXbp0IS0tjSlTpjB37lw6duzIxx9/TJ06dTh58iSDBg1izJgxpKSkMHLkyDLne/kUVnk89cgKJAls/GT0dcTGSwqgc5ZcskGCGiGEKLX79OdlghoXJwWWbHafXkKH5GHV9vp6vZ7k5GRt2uall16iWbNmDB8+HIA+ffq4rdxVVZUGDRpolfMNBgOdOnXS8ltKtzdu3Fjb55133mHQoEHs3LkTcI3WVLQa2GazlZke2rhxIxkZGWRkZLitMt66dStTpkxh2bJlWv4NuLoCbN26lY0bN7Jp0ybS09MZMGAAS5cuZezYsQwePNj3i1XDJLDxk1JOYFM611vunG85U1Ee54ZVp4zUCCFEObIKM/HcfMbJycId1RrYKIrCsWPHSE9PB1w5Nlu2bOHzzz8HICcnR/u9brFYiI+PZ8OGDdr+Tz31FGazmQ8//ND9zJ1O8vPziYiIYOzYscTHxzNq1CgAunXrRl5eHgAXLlzA4XDQpEkToqKisFqt1K1bl02bNmG1Wlm1ahXff/89NpuNyZMna01ML/+u0el0biMtPXr0oHv37lgsFmJjY4mPj2f27NmYTCaysrLYtm0bqamp5TbJLCwspHPnzh6vmaqq1KlTx+trXFUS2PjpbG4udQGn1UL+tjUUH/4NZ0kxOOygN6APjySiSUtqp/VCCQvDqjoxqgo2VDKs5ziKFScqOhQa68LpZKhNmPL7UF0VlnTXVAKdEEIEi+tLuoKOwoBTdVTr70ObzUZsbCzDhrmCp5UrV5KYmKglAb/77rvY7XYAXnzxRdavX6+NtuTl5aGqKvHx8aSlpXHhwgVKSkpISEhAVVWsViszZsygY8eObq/53XffAa6WREePHuX7778nNjaWpUuXugUMjz76KPv27SMiIoIxY8bQp08fevbsSUFBgVuwlZ+fz6233krXrl156623mDdvHgcOHGDPnj0cPnyYjh07Mn78eHr06EFmZibLli1j/PjxHD16lMTERPr27avlBOXn52O1WtmxY4fH4OXgwYMsWrQoULfAIwls/BRTuzZOawmnPv8njvyz7g/abTgu5HNh1w+Y/28fdQY/zseGkx7zbHY5izhutfBgWBJGRe/1ku5gJNAJIUSwKIqCUklbGUXRV+sfeU2bNmXy5Mnaz7Vq1SIuLo5GjRoB8P7775OcnAzAG2+8oT3vxx9/ZPTo0SxYsIDU1FSsViv9+/dn1KhRHpdOX+qnn37im2++Yfbs2QwePJhx48YxatQoPvvsM20UZdKkSSQlJfH0009r+82bN4+5c+fy7LPPotPptKmolStXas/Zt28fAMOGDWPSpEns3LlTmwa7VM+ePRk0aJBba6TMzEzi4+MrHJFp1qwZEyZMqPQ9+ksCGz/9sn0/TTcvLxvUXMZRcI68b/8DdwMVrBA/h53v7Oe5I7KlV0u6g51AJ4QQwZAS3Z4Deetx9am5nI6U6A7V+vrDhw+npKREy1s5evQox44d49ChQ4BrROStt95i48aN2j6qqrJ27Vrq1KnDiBEjtNGaJk2aeBXU7N69m8cee4zp06cTGRkJwODBg9mxYwf33HMPkydP5o477iAlJaXMvrGxsSxfvpyoqCj++te/lnv81157DXCNKOXk5JCVlVXmOfPmzWPHjh306dPHbfsXX3zBnXfeWel7qAkS2PipXsPrMO8vG9GWp+TgbrindqXP2+8006b+o14t6Q52Ap0QQgRDm6RBnCraQ4ElG/fgRkeMqR5tkgZW6+tfOtIBlS/3BtdI09/+9jfGjh3LypUrmTx5MjfddBMZGRn079+fHj160KdPH5o1a+a2n8Ph4MyZMzz33HO8/vrr3HbbbRw7dkyrafP222/z9ttv89FHH3HbbbfhcDj48ccfOXTokDY1FhkZyT/+8Q/sdjuHDh1i586dHlcxVVYrx+Fwnwbcvn07y5YtY82aNRXuV1MksPFTTFwcBb7soKu8kI0NlWUHn6N+TIdKp5OCnUAnhBDBYNRH0LPxy+w+vYSThTtwqg50ip560R1okzSwWkeqO3bsSFhYmNZSB1yjHBkZGSxevFjbZrPZKCoqIiMjg2eeeYazZ89y7Ngx8vPzufvuu1m8eDGtW7fG4XDwww8/8Pnnn3P33XczbNgwJk6cqB3HYrEQFRXFunXrCAsL045dupqptBJyKYfDwUsvvUS9evXo3r27tv3mm28GYOLEiSxbtszjyE1JiWtFbulKrUvl5+dz0003aT+rqsrEiRMZPHiw27L0YFLUYHSoCrLc3Fy3Nf9VpSgKERERFA7t6fU+Sx6PAq+bd+qIMSV7nE5SVZUV+8dhtp/zeIQIw3Wkt3jfq7nmqzHxWFEUkpOTyc7ODkozNlExuT+hrzrvUUFBATExMQE5Vqj//tqwYQNms5nmzZvTrFkzj/Vc8vPzMRqN2lSTNy6vY+MvVVUpLi4ud+VTeSwWC3a73evnV8TTZ8JoNJKYmOjVMWTExk/h4eEU4F3dPRXAp+JETgosJz1OJwUigU4Sj4UQV4NQDmqg/NGP8tSuXXm6QnVTFMWnIMVkMrmNXgVb9ZcAvMoFMkoun0pW4cW+Gpf/xZQS3R7Pt7HiBLrSxOMDeesotp3BbD9Hse0MB/LWs+7IK9gc5gCcvxBCCFFzZMTGT5dWcqwuJbZCtp+cx8kLZUdV/Emgk8RjIYQQVxsZsfGTz3POVeiT4cTKwXPlj6oA9Gz8Ms3jehJlTCDCcB1RxgSax/WkZ+OX/E48FkIIIa4kMmLjp6KiIt92qHKLhIpHVUr/8zaBLhQqdwohhBCBJiM2frq84VjNKjuq4m0QEgqVO4UQQohAk8DGT6U1BYKldFSlKvxJPBZCCCFCkQQ2fiptcuY1Z3nlv6vOn1GVNkmDiDElU/ZjUDOVO4UQ4mpit9s5fvw43377LZ988om23eFwYDabvfoj1G63YzZXvCL1+PHj2v+fP3++yufrDYvFQmZmps/75efn89577wWlPpXk2PgpPDycymsJX6IKycMVHMyvUZVgVu4UQogrlaqqPPHEE2RnZ1NSUkJBQQH5+fnYbDZSUlJITEwkISGB/fv306JFC3777TceeeQRIiIiUBQFq9XK0aNHad68ORaLhaysLJo0aQK4ApuGDRsyd+5c7fW++uorTp48yahRozh+/DidOnVi27ZtnDx5kr/+9a989dVXxMbGup3j3LlzeeWVVyos9Gc2m3nkkUd45plnPD7nxIkTpKens2LFCtq3b+/1NYqIiGDBggXceOON9O7d2+v9AkECGz+ZzWbvgxqoUuKwgg61nEZv0WF1/R5VMeojfE48FkKIa1lpCwOn00lcXBy1a9dm/PjxHntFpaamkpGRof188OBBhg4dytq1a9m7dy9jx45l7dq1Hl9v/fr1NGjQAIBffvmFNm3akJKSQkpKCl27dmX48OF8+eWXGC6pam80GhkwYABvvfWWx+O+/fbbbhWQ58yZwwsvvFCmh5ROp+O+++5z2+ZwOJg+fbrH5p1hYWE8+eSTHl+7Oklg4ydfkodV8GNVVPWToEYIccWqod+tubm5dO3alaioKLegIC8vj7Vr17pNQTmdTi5cuMD27dspLCzk3nvvBVyjMrm5uaSlpWG32zl79qzWrLJp06YsXLhQO4bZbGbjxo2sWrUKcHX4vv3227XHX3nlFb7//nsMBgNOp1M7J51OxzfffMOvv/4KwOnTp0lISECn05Gbm0tcXBwlJSUMGTJEO5bRaKRTp04sWbIEgKeffpoWLVowevToMtdh4MCBZb7/WrduTWFhYYXXLzExke3bt1f4HH9JYOMnT91Ry6NAlf7hlTdaA1BozZEiekKIa5bicBCdfYrwggJwqqBTKImJoTC5DqoPv5t9kZiYyJ49e8psr6y7d1FREXl5eRw4cEAbsdm2bZvbiM3WrVuZNGmS236ffPIJ2dnZ3HbbbRiNRsxmM+Hh4W7BD4DVamXq1Kk8+OCDgCuwufvuu7URm86dOzN//nySkpK44447mDVrljYKVKphw4Z069YNgJkzZ7Jw4UISEhKYOXOm9hyTycSnn35K3759adiwYZn3mZGRUea4pb7++mtefPHFch8LJAls/FTaXdVrAf2rQrp3CyGuTYrDQcKBQxhKLG7pAFFnzmK6cIEzzZtWW3AD8NNPP7mNdlitVnQ6HW+//ba2bfny5bRs2dKv1xk6dCgPPPAASUlJnDx5ku7du5OZmenWy6m8Jpilf3T36dOHM2fOcOrUKfr06YNerycnJ4f+/fvjdDp59dVXSU9PB6BLly6kpaUxZcoU5s6dS8eOHfn444+pU6cOJ0+eZNCgQYwZM4aUlBRGjhxZ5ly9+UPfU/PPQJLAxk8+BzZ2OwSw9o0U0RNCXIuis0+VCWrANTJuKLEQnX2Kgvr1qu319Xo9ycnJ2rTNSy+9RLNmzRg+fDjgCiguzXlRVZUGDRpozTANBgOdOnUiIsK1SKN0e+PGjd1ep3bt2lpjzPnz59OtWzevGlSWBhCrV68GXCM2S5cu1UZs/v3vf5c7svLdd9+xdetWNm7cyKZNm0hPT2fAgAEsXbqUsWPHMnjwYO8vUpBIYOOnyMhI8n3ZwRDYSy5F9IQQ16LwggKPCzeU3x8voPoCG0VROHbsmDbakZeXx5YtW/j8888ByMnJ0X43WywW4uPj2bBhg7b/U089hdls5sMPP3Q7rtPpJD8/n4iICLc6adnZ2cyaNQudTqfl45QeW1VVMjIyqFWrlrbd2z+6nU4nTqdTC8J69OhB9+7dsVgsxMbGEh8fz+zZszGZTGRlZbFt2zZSU1PLDa4KCwvp3Lmzx9dSVZU6dep4dV7+kMDGTxcuXPBtB6cTAjY8KkX0hBDXIFV15dRUxKlWa0KxzWYjNjaWYcNcqQArV64kMTFRCzreffddrc7Ziy++yPr167XgIS8vD1VViY+PJy0tjQsXLlBSUkJCQgKqqmK1WpkxYwYdO3b8/e2qPPPMM1x//fUUFhbyzTffaMu7n3rqKVJTU92CGnAFPMuXL2fLli3k5eURHh5O7969OX/+PImJiQwc6FpR63Q6GTlyJI899hjz5s3jwIED7Nmzh8OHD9OxY0fGjx9Pjx49yMzMZNmyZYwfP56jR4+SmJhI3759tZyg/Px8rFYrO3bs8Bi8HDx4kEWLFgXwLpRPAhs/OQNccM97UkRPCHGNUhTQVRKw6JRqXSXVtGlTJk+erP1cq1Yt4uLiaNSoEQDvv/8+ycnJALzxxhva83788UdGjx7NggULSE1NxWq10r9/f0aNGuVx6fSbb77Jzz//zKpVq1i6dCnjx49nxowZrFu3jt9++63cJd1ZWVmMGjWKZ599lieeeILu3bvTq1cv/vCHP7Bu3ToiIyMZN24cjz32GK1atQJg3759AAwbNoxJkyaxc+dOdu7cWebYPXv2ZNCgQcTHx2vbMjMziY+Pr3BEplmzZkyYMMHj44EigY2fKqsQWYbFAhUUTKqYjnB9DHqdQYroCSGuaSUxMUSdOVvudJT6++PVafjw4ZSUlBAeHg7A0aNHOXbsGIcOHQJcIyZvvfUWGzduvHheqsratWupU6cOI0aM0EZrmjRpUm5Q43A4eOONN/j000+ZP38+DRs2ZOzYsYwcOZJRo0Zx8OBBlixZgslkKpM8/OuvvzJgwAC3bREREQwcOJCXXnqJqKgo7HY7zZs31x5/7bXXANeIUk5ODllZWWXOad68eezYsYM+ffq4bf/iiy+48847fbiC1UcCGz+ZTCZ8Cm38+Asi0hhHv+bvoCiKJAwLIa5phcl1MF24UCaBWAXs4SYKk6s3l2PlypVuP1e23BtceTl/+9vfGDt2LCtXrmTy5MncdNNNZGRk0L9/f3r06EGfPn1o1qwZ4Brd2bRpE6tWrdISfY8cOUJYWBgbNmygdevWnD59mvr167u9TnFxMT/99BPTpk0DXDMLx44d43//93955513GDZsGAcPHuSrr74qtxZbZW0QHA6H28/bt29n2bJlrFmzpsL9aooENn7yeelalVdE6UiOuonMnPlkFWaiqg4URU9KdHvaJA2SkRshxDVF1es507xpjdexAejYsSNhYWGYTCZtW15eHhkZGSxevFjbZrPZKCoqIiMjg2eeeYazZ89y7Ngx8vPzufvuu1m8eDGtW7fG4XDwww8/8Pnnn3P33XczbNgwJk6cyG233cZXX33Fnj17mDp1Kt9//z3Z2dmMHj2a6dOns2DBAkaMGEF0dDS33nor/fv3p3PnzixbtoymTZuSkpICuEZvVqxYwbhx44iMjGTRokVMmTKFu+66i27dujFo0CB69uypnXdJSQlwcaXWpfLz87npppu0n1VVZeLEiQwePJgbb7wx4Ne6KhQ1GB2qgiw3N7fMsF1VKIpC7dq1OTPoDq/aKqjAF49HVWFllI7osLqASqE15/cjXXwsxpTMnY1eIsxQ1Smuq5eiKCQnJ5OdnR2UZmyiYnJ/Ql913qOCggJiAjVlFMJV3QE2bNiA2WymefPmNGvWzOMfxfn5+RiNRrceT7t27eKzzz6jX79+pKWlue1rs9nIzMxk3bp1DB06lOuvv55ffvmFs2fP0rVrVwA++OADOnXqRIcO7otNsrKyWLx4Mb169aJ169badlVVKS4u9mpZObim3ex2u9fPr4inz4TRaCQxMdGrY0hg4wdFUVwNzUb18z6wGRPp1aiNgh6TIRq94sqncao2Dp3biHtQc5FeMWEyRMsIzmXkizO0yf0JfVdMYHMNK69A35UqEIGNTEX5qU6dOhyv/GkXeTlac/+NH2LQhWt5NCv2P4mnoAbAoVootlk4kLeeU0V76Nn4ZQluhBBCXHOqv7bxVc5ut1cQbpS7g1dP+/rQ38jMmY/NYUZVVVTVUflOADgpsGSz+/QSX85KCCGEuCpIYOMnvV7v1TSUxssRm2LbGQ7krWfdkVewO0tQFF8S4Vw9pKDy7HYhhBDiaiJTUX7yeV7Tp+Q2JwWWk+w+vYSU6PYcyFsPHjp9X67EXsCK/U/K6ikhhBDXFBmx8ZPPlYd9rlSsklXwE22SBhFjSsbbW+ZQrRTbzmC2n3Mb/bE5fCwoKIQQQlxBJLDxk8HXpdtVWI5ocVxAr5jo2fhlmsf1JMqYgF4Jq3xHN5J7I4QQ1clut3P8+HG+/fZbPvnkE227w+HAbDZ7lRpgt9srrWjvdDo5e/Zshc8pKSnh5MmTZGRk8Oqrr7J48WLsdrtWo8ZbFouFzMxMn/YB17L19957LyjpEDIV5afLKzBWymqFS4o6efUaqpWVB57QppTubjoFVVVZ/3+vUmDJxtvpqdLcmw7Jw3w758tI1WMhxLVMVVWeeOIJsrOzKSkpoaCggPz8fGw2GykpKSQmJpKQkMD+/ftp0aIFv/32G4888ggRERGuEiFWK0ePHqV58+ZYLBaysrJo0qQJ4ApsGjZsyNy5cwFXENOgQQMOHjxIRIQrlWDlypX861//4osvvnArEnip22+/HZPJRKNGjWjevDmKovDNN98wfvx47HY7YWFhREdHc/bsWR5++GGeeeaZco9z4sQJ0tPTWbFiBe3bt/f6GkVERLBgwQJuvPFGevfu7cvl9ZsENn7yOcemipWHzfZzAG7LuXs2fpndp5dwsnAHTtWBgg6L4wIO1eLxOE7VUaXAxOYws/v051L1WAhxzVMUhXHjxuF0OomLi6N27dqMHz/eY0uF1NRUMjIytJ8PHjzI0KFDWbt2LXv37mXs2LGsXbu23NfS6XSYTCYtqMnPz2fSpElYLBbuuOMOwNWAc8WKFW5F/QwGAx999BGpqaksX76c+++/H4C+ffvyxz/+kQcffJABAwYwfPhwrV3DnDlzeOGFF9BfVrVZp9Nx3333uW1zOBxMnz7dY+POsLAwnnzyyQqvY3WRwMZPkZGRnPNlB19bMJRxcUqpQ/Iw7b/SYGXF/icptnkObBRFX6WgZt2RVyiwnOTSWjpSM0cIESpqaiQ5NzeXrl27EhUV5VYBOC8vj7Vr17pNQTmdTi5cuMD27dspLCzk3nvvBVyjMrm5uaSlpWG32zl79ixpaWmAq2v4woULAdixYwcHDx7E6XTy2Wef0aZNG6ZNm8bjjz/OiBEjAHjmmWeIiYlxC2oA7VoUFhYyefJkoqKi6NWrF4WFhfz8889ax/HCwkKt9YLRaKRTp04sWeJKWXj66adp0aIFo0ePLnMdBg4cWKbPVOvWrSksLKzw+iUmJrJ9+/YKn+MvCWz85HPysMMBfvcwKTulVPohrnj1lI6U6A7lbK/Y7tOflwlqSs/j0iBLCCFqklpSjHPpAtj5X3DYQW+Atreiu/8hlPDqaTGTmJjInj17ymyvrAlmUVEReXl5HDhwQBux2bZtm9uIzdatW5k0aZK2z2+//cbatWtxOBx89dVX5ObmEhcXx/DhwwE4d+4cq1evZvPmzWVerzSf5vPPP8fhcPDCCy/QpUsX3n77bW677TYaNmyI2Wzm+PHj1KnjahjasGFDunXrBsDMmTNZuHAhCQkJzJw5UzuuyWTi008/pW/fvjRs2LDM62ZkZGgjQJf7+uuvefHFFz1c2cCRwMZPPjfBDFBjNk9TSm2SBnGqaE85uTc6Ykz1aJM00OfXyirMxHPV48Dk7QghhC/UkmKcU5+B7OOuPlGlNq7GuXcXuuffqLbgBuCnn35iyJAh2s9WqxWdTsfbb7+tbVu+fDktW7as8msMHTqUoUOH0qRJE+bMmQPA8OHDad++PSaTidzcXGJiYujTpw+nT59m/vz5dO7cmdOnT5Obm8vIkSMZMGAAq1atYufOnbz11lssX76c1atXs2bNGv785z9z/fXXa/k9Xbp0IS0tjSlTpjB37lw6duzIxx9/TJ06dTh58iSDBg1izJgxpKSkMHLkyDLne/kUVnl8/s6sAgls/FRQUODbDk5nAKajPE8pGfURZXJvdIqeetEdaJM00OcpI2+qHlc1b0cIIarKuXRB2aAGXL9jc07gXLoA/ZCHq+319Xo9ycnJ2rTNSy+9RLNmzbTRlD59+ritmlVVlQYNGmgdsw0GA506ddJyZ0q3N27cuNzXmzFjBo0aNSIsLIx//vOfdOrUiVtuuYXly5fTqFEj7rvvPm1qKCEhgb/+9a88+OCD2qhKSkoKdevWZciQIdStW5fExESWLl1K+/bt3c7zu+++Y+vWrWzcuJFNmzaRnp7OgAEDWLp0KWPHjmXw4MGBvIzVQgIbP10+x1ipgCx9q3hKyaiPKJN7U1WuRp8VR+FVydsRQgi/7Pyv59+nTqfr8WoMbBRF4dixY6SnpwOuHJstW7bw+eefA5CTk6P9XrRYLMTHx7NhwwZt/6eeegqz2cyHH3542ak7yc/PJyIigv/+97/MmjULi8VCXFwc3bt3Z/78+Tz22GMYDAZycnIYMGAAiqJw+vRpbTGLTqejV69edO7cmXr16mnHPnv2LOHh4VoX7qKiIlq1asXixYu15/To0YPu3btjsViIjY0lPj6e2bNnYzKZyMrKYtu2baSmppbbybuwsJDOnTt7vGaqqmrTXtUp6IFNUVERJ0+eJDk5mVq1agX7dHwWHh7u2w5+T0X5NqUUiICjOvJ2hBCiqlRVdeXUVMRRvSPJNpuN2NhYhg1zTcOvXLmSxMRELQn43Xffxf57b8AXX3yR9evXayMjeXl5qKpKfHw8aWlpXLhwgZKSEhISElBVFavVyowZM9iyZQvdu3dn06ZN2uqjefPmAfDOO+9w8OBBtm/fzvbt28us0DUYDNSuXZtt27Zp2/7yl7/QrVs3bdTls88+Y+XKldrj8+bN48CBA+zZs4fDhw/TsWNHxo8fT48ePcjMzGTZsmWMHz+eo0ePkpiYSN++fbWcoPz8fKxWKzt27PAYvBw8eJBFixb5d+G9ENTA5vvvv+fjjz8mPj6e06dP89hjj9GpUyeOHTvGhx9+SE5ODj169OChhx7SPpx79uxhxowZFBQUcP/999OvX79gvgUsFs8rkMpls1VhybdChCHWryklf1RH3o4QQlSVoiiuROGK6Kt3JLlp06ZMnjxZ+7lWrVrExcXRqFEjAN5//32Sk5MBtBVIAD/++COjR49mwYIFpKamYrVa6d+/P6NGjSqzdLpjx44ATJw40W37+fPnmTdvHm+++abHFUY6nY78/Hwt0ALXiM2WLVt45513ANfAQocOF/8w3bdvHwDDhg1j0qRJ7Ny5k507d5Y5ds+ePRk0aBDx8fHatszMTOLj4ysckWnWrBkTJkzw+HigBC2wKS4uZubMmbzyyitcf/31bNy4kQULFnDLLbcwbdo02rZty7hx4/jkk0/YuHEj3bt3p6CggGnTppGenk7nzp157733aNSoEampqcF6G76P2PhaqRjQKybSW7wftOmeQOftCCGE39reChtXl9+mRqdzPV6Nhg8fTklJifYdcPToUY4dO8ahQ4cA1x+9b731Fhs3btT2UVWVtWvXUqdOHUaMGKGN1jRp0sRjPZjL2Ww2xo0bxx/+8Afatm3rduwTJ064rUjyZsRm1apV2uOvvfYa4BpRysnJISsrq8zrz5s3jx07dtCnTx+37V988QV33nmnV++hugU1sPnTn/7E9ddfD7gSpgoLC8nMzKS4uJgRI0ZgMpkYMmQIs2bNonv37mzZsoW4uDgeeOABFEVh4MCBbNiwIaiBjc8jNlWgKIGZUvJHIPN2hBDCX7r7H8K5dxfknHAPbnQ6qFsf3f0PVevrXzqFA5Uv9wbX7/G//e1vjB07lpUrVzJ58mRuuukmMjIy6N+/Pz169KBPnz40a9ZM28dut2tlRU6dOsXYsWMpLi5m+vTpWK1WioqKUFWVffv20a9fP3755RfCw8O1XJ3KRmzKqyZcWRuEyyvub9++nWXLlrFmzZoK96spQQtsEhIS6NKlC+C6catWreLWW2/l6NGjtGjRQisTff3113PixAnAFRG3bt1a+1Jt1qyZVsioPDabzW3eUVEULQM9EF/MiqL4XsemCgy6cO31SgUzuLiSgprSc72SzvlaIvcn9IXqPVLCI9E9/8YldWx+rxFWzXVswDVFFBYW5tbOIC8vj4yMDLdEXJvNRlFRERkZGTzzzDOcPXuWY8eOkZ+fz913383ixYtp3bo1DoeDH374gc8//5y7776bYcOGMXHiRE6dOkWXLl3o0qULZ8+epVevXnTt2pWpU6cSFRWFw+EgOTmZDh06UFRUxPDhw7URJKvVWmbE5nKfffaZtqrrUqX9pEpXal0qPz+fm266SftZVVUmTpzI4MGDufHGG32/mOXw97OmqMHoUHWJ//u//+PVV1/FYDDw7rvv8sUXX2C1Wvnf//1f7TmjRo3i/fff56OPPqJFixZa9caSkhIeeeQRrafG5RYvXux20xo3bsy0adMCev5Op5MT6bfizW1QgS/GxfjcCDMmvA6juizEai8m4+BsDudu1aaDmiTeTudmfybMUH3/iIUQojocPnyY6OjogBwr1EeS169fT3FxMS1atKB58+Ye67nk5+djMBi0VUf5+fnUrl0bgKysLK1KcHVSVZWioiKvF/RYLBZsNltAFgAVFhZqdXWqKuiroq6//nomTJjA3Llz+eijj6hTp06ZJdRhYWFYrVb0er3bevvS7Z5cnlxc+qHPzc3VstX9oSgK1113nW87qaqPgY2OOpFtOXbiMGsPTyxTAfjn48s4fPq/3NVkouS6lENRFOrWrUtOTk5QusyKisn9CX3VeY+sVqvv/fauUKV9ncA1leOpgXJpa4TS6xIZGan9f1JSUrnXy2g0Bvw6mkwmr49Z2s8qEOdgtVrJzs4us91gMJCYmOjVMYIe2CiKQpMmTRgzZgxjx45lyJAhHD9+3O05ZrMZg8FArVq13ArilW73xGg0eqwzE6h/oD5XUfTxdUtXHe06tbjCtga7Tn0u1X8r4Co0KF+coUruT+iTeyRqir+fs+qvbezBnj17mD9/vvZzaYBSv3599u/fr20vLTpUq1YtmjZtyoEDB7THjhw5QlxcXM2ddDl8vgE+1rGx/95V+0TBdiprayCEEEJc64IW2CQnJ7Nu3TrWrVvHmTNnWLhwIW3btqV9+/aYzWa+/fZbAL788kvatGmDTqfjlltuYe/evezatQu73c7y5cvdlrsFQ3UPoxbbz3Igbz0l9vMVPq/EXoDVXlyt5yKEEIFWEwswxJUhUJ+FoAU21113HePHj2f16tWMHz8eq9XK448/jl6v59FHH2X27NmMGjWKn376iYceci3bi4mJYcSIEUydOpXRo0dz8uRJBgwYEKy3AOCWFe+VKg2xOVHLrfp7kUO1sv7/XsXmMFfh+EIIUfMiIyMpLCyU4EbgdDopLCzUcoz8EfRVUZ6cP3+ew4cP07x58zJZ86dPnyYrK4uWLVv6XiAPV/JwIEZaSpePFwzt6f2qqLG1AtbhuywdzeN6Sq7NJRRFITk5mezsbMkPCEFyf0Jfdd8ju91OcbGMNvujsoU0V4rIyEiPebNGo/HKSR72JDY21q3U86WSkpJISkqq4TMqnzdt2t348YtBQVfJyI0r10YCGyHElcJgMBATExPs07hiyR8HZQVtKupq4fNUVBVaKpSKMFyHXqn49ZyqQz7cQgghrlkS2PiptEKj16o8l6wjJeZmTIaKi1kpSvU2fhNCCCFCmQQ2fqqojk65qjSacrGLdkp0ezzfNh0p0eVP3wkhhBDXgpDNsblS+BzY+FDQT1WhVliC1kUbwKnaUVDKqWhzMfgRQgghrlUS2PhJVV3pvF6nENvt4KEacll6+rV4FwCbw8y6I6+UW31YQU+T2K60rftHrWGmEEIIcS2SwMZPOp2O82GRxFmLvVry7W3ysKpCtKmu9vPu0597aKkAKg6OF/yXkxd+BlQURU9KdHvaJA2S/lFCCCGuKRLY+Mlut2N02L0LasCVPOzFEnFFAad6MTE5qzATzy0VwOq8wKUrwQ/kredU0R56Nn5ZghshhBDXDJ+Sh4uLi9m8eTP/+c9/2LBhA3l5eWWec+bMGWbNmhWwEwx1drudcKcPncJ9WLGkqk6t8Zyqlt8J1jNXc8zdp5f4uJ8QQghx5fJ6xObkyZO8/PLLFBQUaFUODQYD48aN49ZbbyUnJ4f//Oc/bN68mZiYGEaNGlWd5x0yIiIiMKg+LOH2YVXUpUu3FaUq1YqlYJ8QQohri9eBzYIFC6hVqxYvv/wy9evXp7i4mJkzZzJjxgx++OEHvv/+e2JjYxk2bBg9e/asznMOKSUlJTgUHTpfghsvGZRwbA4zRn0EKdHtOZC3joqmo8pTWrBPatsIIYS4Fng9FbVv3z4efPBB6tevD7h6OvzpT3+ioKCAffv28ac//Ym///3v3HPPPRi9XvVz5TMYDNUS1AAUWE+w7sgr2Bxm2iQNIsZUz+djSME+IYQQ1xKvA5sLFy6U6c9U2t/j6aef5u677/a9pstVQK/X+5ao5GOQUZonY9CF07Pxy9Q2NfBhbynYJ4QQ4triUySSnZ1d7l//J0+eLNN2vkmTJv6d2RXCbDb7toPPoydODuat40TBjyiKnuRabXCqNgqtOZXuGR1WVwr2CSGEuKb4FNj8/e9/L3f7+++/X2bbZ599VrUzusJERUVx3pcdqjAtpOLEbD8HwKFzG1G8HiOqWjNMyckRQghxpfI6sHn55Zer8zyuWIWFhb7toKpVCm4uOQAq3i39LrSeYvfpJV6tirI5zOw+/TlZhZmoqkOK/AkhhLgieR3YtGrVqjrP44oVERFBgS87eFmgLzC8W+7tqV2DFPkTQghxpZHu3n7S+xqk+NAEMxBKl3tXxHO7BinyJ4QQ4soigY2fCgp8Gq/xqUBfIHiz3Lvidg2uUR8hhBDiSiCBjZ9MJpNvO9RoUm7ly729adfgzaiPEEIIEQoksPFTeHi4bzv4HCBcfosUdIoBKm27qSPGVK/S5d6KolTarkGK/AkhhLhSXHsV9QLM5y98H5/fPK4nJwt34FQd6BQ99aI7cGN8P/aeXaltV1Aw6qOwOYtRVaf2vDZJA8sk/Za3lNvVrmE9bu3BNVLkTwghxJVDAhs/+TxF48Nyb71ion3dh+iQPKxMQNIheVi528sLXCpbyt0maRCnivZQYMnGPbjxbtRHCCGECBUS2PgpPz+/2o7tUK2sPPBUhfVkLg9iygtqvFnK3bPxy+w+vaTM6FB5oz5CCCFEqJLAxk86X5dv+zTCo1JsO8OBvLXkXPiFu5q84nOQ4c1S7g7JwzDqIzyOAgkhhBBXCkke9lNkZKRvO1QxYCi0ZrPz1CKf96vKUm4JaoQQQlypJLDxk8PhXXsDjR9Bw9HzW33K6ZGl3EIIIa41MhXlJ58DG4cDDFW77Ha1hOX7x6Erp4+TpyRiWcothBDiWiKBjZ98LtDnZ5+okt+7fB/IW0/OhV9IimxJdtGu31c76TDqIrE6igAVRdFj1EXgqnlT3qiMLOUWQghxdZHAxk9OZ3m1XyoQsNERJ4XWbAqt2ZdtP3v5C6JT9DhVB+7BjSzlFkIIcfWRwMZP3jbBDF4Wi4pTdVDbVB+70yxLuYUQQlzVJLDxU0lJiVfP8zQZVDNU7E4z/Vq8K0u5hRBCXNVkVZSffE4ettmq50QqUbr6SYIaIYQQVzMJbPxk8HWFk68F/QJEVj8JIYS4Fkhg4yeLxeLbDr4mGweErH4SQghxbZDAxk8+j4L4sdxbQUeE4TqijAk0va4H0WH1qPwW1vzqJyn4J4QQIlgkedhP3q6K0tjtEBZW5ddLqXUzN9V9EKM+4veu3RcbV4KCw2nF5ixGRUVBIcaUwh0Nn6721U+VdRAXQgghaoKM2PipJkcnVJwcPL+OpXv/wk8nZwPQIXkY/Vq8S++mr2PUR2B1FqHiBFRUnORbsth0bBo2h7nazr+0g/iBvHUU285gtp/7vXnnetYdecXjawshhBCBJiM2foqOjibflx38GK0ppeLg0LlvyS3eT8/GL2PUR/BL7pIKunhnsf7IJO5s/OIlIz2BG13xtoO4EEIIUd1kxMZPxcXFvu3g6/LwCpQGDVBZF2/Itxxn3ZFXKLaeC/joSlU6iAshhBDVQQIbP/k8lRPQqSsnR85vZvm+Jyi2Xd5KoawCy0k2H3uz0tEVX0gHcSGEEKFEAhs/RUZGBvX17c4SzPazeFfXWKXAklXBc30fXZEO4kIIIUKJBDZ+KikpwafKNEH+glcrCYCqMrqSEt0ezx8lqaEjhBCi5khg46fw8HDfdghS5eFSChUHVlUZXWmTNIgYUzJlP07SQVwIIUTNklVRfnI4HJWECpdR1SCO2uiIMaWQb8mCcseZqja6YtRH0LPxy241daSDuBBCiGCQwMZPJSUlvgU2TmeQRm1coyd3NHyaTcemUWDJxj248W90xaiPoEPyMDokD5Nmm0IIIYJGAhs/RUZGct6XHfxoqeCLMF0tjPrwckdPqnt0RYIaIYQQwSKBjZ9KSkp828HprJHgxqALo1+Ld8sdPZHRFSGEEFcrSR72k9VqDfYplEtF9SpokaBGCCHE1UQCGz/FxMT4tkMN5ddI7RghhBDXIgls/FRQUODbDk6fqt5UkdSOEUIIcW0Kao7Njz/+yNy5czlz5gwNGjRg3Lhx1K9fn2PHjvHhhx+Sk5NDjx49eOihh7TRhz179jBjxgwKCgq4//776devXzDfAtHR0VzwZYcayK9RUHA67dgcZgy68HJHbiS3RgghxNUoaIFNTk4O//znPxk9ejStWrVi9uzZfPzxx7z00ktMmzaNtm3bMm7cOD755BM2btxI9+7dKSgoYNq0aaSnp9O5c2fee+89GjVqRGpqarDeBhcu+BTWgM0GRmPAXl+PCSd2VC72a1JxcOj8Bg6f34RJH41OZyAluj03xqez9+yKgHX1FkIIIUJN0KaisrKyGDp0KLfffjuxsbH06tWLI0eOkJmZSXFxMSNGjKBu3boMGTKEDRs2ALBlyxbi4uJ44IEHSE5OZuDAgdpjwWIw+BgbBnqURFFRPTR1UHFQ4jj/e/fudaw6+BQH8tYGrKu3EEIIEWqCNmJz8803u/188uRJkpOTOXr0KC1atMBkMgFw/fXXc+LECQCOHj1K69attSmUZs2asXDhQo+vYbPZsNls2s+KohAREaH9v78URdHOM1icqh1vG2C6nlvmCFpX75vrDQ/w2QVf6X2WabfQJPcn9Mk9Cm1yf8oKiTo2drudlStX0q9fP3JyckhMTNQeUxQFnU7HhQsXKC4upn79+tpjERER5OXleTzu0qVLWbJkifZz48aNmTZtmtvx/eVz8rCPDSYrpSjexTUVcnKqeBfJycmBOKOQVLdu3WCfgqiA3J/QJ/cotMn9uSgkApvFixdjMpno0aMHixYtwnhZDkpYWBhWqxW9Xu829VO63ZPLk4tLI9rc3Fzs9vJGL3yjKIrvy70DLUCBks1u4eTJk1dd1K8oCnXr1iUnJ8fnruWi+sn9CX1yj0LbtXJ/DAaD14MSQQ9sfvnlF7755htee+01DAYDtWrV4vjx427PMZvN2mOXjpCUbvfEaDSWCZJKBeoD4HOOja/Pr4ROMeBQ7ZTf1NJ7iuJarXW1/sNQVfWqfW9XA7k/oU/uUWiT+3NRUOvYnD59mvfff59Ro0ZpU0zNmjVj//79bs+x2WzUqlWLpk2bcuDAAe2xI0eOEBcXV+Pn7ZcA17Ex6qKIMSXj362UujdCCCGuDkELbKxWK6+//jq33HILt956KyUlJZSUlHDjjTdiNpv59ttvAfjyyy9p06YNOp2OW265hb1797Jr1y7sdjvLly+nbdu2wXoLADh9DVQCXMfG5izG7iyhtimFSEMc4YZYFMp7DQWdYoAyvcj96+othBBChJKgTUXt3LmTEydOcOLECdavX69t/8c//sGjjz7K+++/z4IFC1AUhYkTJwKu9gUjRoxg6tSphIeHExUVxWOPPRakd+Dic66OwxHQ4MahWii2WYBzxJiSubPRSyiKUm737hvj+7H37Mpq6+othBBCBJuihuik3Pnz5zl8+DDNmzcnOjra7bHTp0+TlZVFy5YtCQ8P9/nYubm5bsvAq0pRFBITEzl5321lxkHKowJf/DW6yv2iFHS/L4DyNEqko3lcTzokD7v4mh4qDF8rlYcVRSE5OZns7GyZfw5Bcn9Cn9yj0Hat3B+j0eh18nDI9oqKjY2lQ4cOZYIagKSkJNq3b1+loCbQfA4O/GiC6SrEV9HUl5OThTvctng6v2shqBFCCHHtCdnA5krh81RUAEaKKuJUHeVG7VdzJC+EEEKUCvpy7yudXq9HpWxKrkcB7BNVHkXRa6MxNoeZ3ac/l95QQgghrhkS2PhJURTyTFHEW4q8C25UNfD9ojQXl23bHGbWHXmFAstJLi1NfCBvPaeK9tCz8csS3AghhLjqyFSUnxwOB3lhPuT6BLiOzUWK27Lt3ac/LxPU/H4CWm8oIYQQ4mojgY2f9Ho9zQrPej8VVU2jNbXD6tOz8UvaKMyJgh14biJVNslYCCGEuBrIVJSf7Ha790FNNSqwZvHVwecI00dhcxRjtntuDgoXk4xldZQQQoiriQQ2fgpEM81AUHFitudVGtCUujTJWAghhLhayFSUnzw12fTIjzo2gSO9oYQQQlydQuFb9ormc30Yh6N6TsRr0htKCCHE1Uumovzk83RO0KZ/FCINcaTE3Cy9oYQQQly1JLDxkz7A3bqrS6QxnvQW7wb7NIQQQohqJVNRfrJarb7tEJQRG8mpEUIIcW2QwMZPV8KITYwpWXJqhBBCXBMksPHTlbBkWtonCCGEuFZIYOMnp9Ppsb5vuWp4VZReMWHQ+dDyQQghhLiCSWDjJ0VRfKs8XMNTVyZDrXJHlXxepi6EEEJcAWRVlJ8sFgsqeB/cOJ01GNwopETfrP1kc5jZffpzsgozUVUHiqInJbo9bZIGyVSVEEKIq4IENn6KjIzknC871FhQ416Iz+Yws+7IK2U6fh/IW8+poj30bPwyBl242+iO9JISQghxpZHAxk8+T+nY7WAI/GWvHdYAu2rGqTrQKXrqRXdwK8S3+/TnZYIaFycFliyW7/8rRl0EoLgaaTqLUVWn26jO5YGPEEIIEWoksPGTzWbzbYdqCGoU9FichegUA/Vq3Uzbug+WmVrKKsykbFBzkd1Zgt1ZAlCmkeaBvLUczNuASR+NTmeQ6SshhBAhSwIbP4VCHRsVByX28wAcOr+Ow+e/pcl1d9C2zhCM+ghUVUVV/VuNpeKgxHEeHO7TVxLcCCGECCWyKqqm1cBqJBUHh859y7ojr2BzmF0rt5RABmBOCizZ7D69JIDHFEIIIfwngY2fdDofL6HTGYBXVVCoPFC5NPhIiW5PYG+3k5OFOwJ4PCGEEMJ/Etj4yefAJiDJtyoq3kwtXQw+2iQNIsaUTCBvuVN1SD0cIYQQIUUCGz/5nDxcw4FAafBh1EfQs/HLNI/rSZQxgQjDdRgU/yoSK4peVkkJIYQIKZI87CeDr6ucajjZ+NLgw6iPoEPyMDokD0NVVezOkt9r22RV4cjSMVwIIUTokREbP1mtVt92sFiq50TK5Tn4UBQFoz6COxu9hF4x+XzcS4v/CSGEEKFCRmz85PQ1GTgsrHpOpAzvgo8wQyQmQzTFNs8Bl0EXjklfy2PxPyGEECJUSGDjp4iI0PtyjzIm+BR8pES350DeeqC8IE1H49g7tOkryakRQggRyiSw8VOVkoerOTjo2/wdnwKQNkmDOFW0hwJLNu7BjfuojwQ1QgghQp0ENjWt2oMD349fumJq9+klnCzcIVNOQgghrlgS2PjJ5zou1T5io7J4zwhqm+pzR8OniQy7zqu9Ll8xJaMzQgghrkSyKspPPgc2Aak8XBmVfMtxVh18iiJLXuVPv4wENUIIIa5UMmLjJ6PR6NsOvlYq9oNTtbPq4FNEGK/zqiO3jNQIIYS40klg4yeLr3VparjysIqDYtsZjx25bQ4zu09/TlZhJqrqQFH0XgVBQgghRCiSqSg/6X2tJBy0EZGyHbltDjPrjrzCgbx1FNvOYLaf04Kg0s7gQgghxJVEAhs/+dwEM6jcO3LvPv05BZaTwOWjSGWDICGEEOJKcCV9K4ckRVHKLWvnUY0kD1fw8pd05M4qzKRsUKM90y0IEkIIIa4EEtj4qaioCKcvtWNqOMfmcqVNMVVVRVUdFT730iBICCGEuBJIYOOn6Oho9B5HPcrhazfwgNJRr1Z7wDXSpCgV5wdd2hk8UCRQEkIIUZ1kVZSfLBaLb7V+nU7wNeE4QBQUThT+yMn9maREt6dOVEuOnN/i8fl1o9oE5HVl5ZUQQoiaIoGNnwy+jsAEsU6MioMS+3kADuStxaiLrPD5R/MzyCna7VcQUrry6vIkZU/Lz4UQQgh/yFSUn6rUUiFE2JzFFT7uUK1+L/+WlVdCCCFqkgQ2fgrNlgqBVvUgRFZeCSGEqEkS2PjJ4ah4ZVEZQcqv8Z/vQYisvBJCCFHTJLDxk81mC/Yp1Bhfg5BgrbwSQghx7ZLAxk8+t1S4IqeiXKoShKREt6eij5nFXsCK/U+yI3uetHAQQgjhNwls/GQymXzbIah1bPyhIyW6g897tUkaRIwpGU8ftUAkKAshhBClJLDxk885NldoPkmMKZk2SQN93s+oj6Bn45dpHteTKGMCesVTICirpIQQQvhPAhs/+VzH5gqdivKn3oxRH0GH5GH0a/EuYfpaFTxTVkkJIYTwT9ADm4KCAsaMGcPp06e1bceOHeP5559n5MiRzJ8/3y1hdc+ePTz55JOMGjWKlStXBuOU3Th9DVSu0ETZQBTRc93Hiq+XrJISQgjhj6AGNgUFBUybNo3c3Fxtm81mY9q0aTRu3JipU6dy4sQJNm7c6Pb8zp07M3nyZLZs2cIvv/wSpLN38Xkq6oocsVF8D+DKO4qskhJCCFHNgprJ+v7779O5c2cOHDigbcvMzKS4uJgRI0ZgMpkYMmQIs2bNonv37mzZsoW4uDgeeOABFEVh4MCBbNiwgdTU1HKPb7PZ3JZjK4pCRESE9v/+UhTF9+ThK/BLW0HxffWXB/VqtefgufWUP3LjSlAOZGBTeiwJlkKT3J/QJ/cotMn9KSuogc0jjzxCUlISc+bM0bYdPXqUFi1aaAHD9ddfz4kTJ7THWrdurd3AZs2asXDhQo/HX7p0KUuWXExGbdy4MdOmTSMxMTFg7+HMmTO+7XAFTrPE12pEcnJylfe32ovJODibw7lbcTjt6BQF52WXQUFHXFRDerUdS5ih4h5WVVG3bt2AH1MEjtyf0Cf3KLTJ/bkoqIFNUlJSmW1ms9kt8FAUBZ1Ox4ULFyguLqZ+/fraYxEREeTl5Xk8/v3330+/fv3cjgWQm5uL3W73+/wVRSE2NpaKOy5d5gqrPKxTDNxe70mys7OrtL/NYWbt4Ynl9otS0GPSR6PXGagX3YGb6gzibG4+kO//iZe+hqJQt25dcnJyJHcnBMn9CX1yj0LbtXJ/DAaD14MSIVdURafTYTQa3baFhYVhtVrR6/Vuq5BKt3tiNBrLHKtUoD4AVcqxCbngRkfj2n8gr+QIBZYsVFQUFGJMKdzR8GkijLFVvl67Ti320AQTVFTqx9zKzfWGXdxWTf8wXe0drt5/9Fc6uT+hT+5RaJP7c1HIBTa1atXi+PHjbtvMZjMGg4FatWpRUFBQZnsw+dxSwWqFCP9XGAWOjhhTPdonP6StfHI6neh0gckrr6wJZvaFHcAwD48LIYQQvgn6cu/LNWvWjP3792s/nz59GpvNRq1atWjatKlbovGRI0eIi4sLxmlqInwNUsLDq+dEfKRXTEQZE2ge15OejV9yW84dqKBGmmAKIYSoaSEX2LRs2RKz2cy3334LwJdffkmbNm3Q6XTccsst7N27l127dmG321m+fDlt27YN6vn6nKsTgNwef8WY6nNvi+n0a/EuHZKHBaRGTXlkebcQQoiaFnJTUXq9nkcffZT333+fBQsWoCgKEydOBCAmJoYRI0YwdepUwsPDiYqK4rHHHgvq+YaHyAiMt2LC6pcZoalOKdHtOZBX8fJuIYQQIlBCIrBZvHix28+33HILf//73zl8+DDNmzcnOjpae6xXr160a9eOrKwsWrZsGfTAwufRhgBN81SVQy2psaAGXE0wTxXtocCSjXtw48rtqUr/KSGEEMKTkAhsyhMbG0uHDuX/NZ+UlFTuUvFgsFgs2BQdYaqXlXmDPO1SmtPiTUDm7fMqUtoEc/fpJZws3IFTdaBT9NSL7kCbpIE1GmQJIYS4+oVsYHOl0Ol0WHUGwhyel51ftkP1nlAlKstpsTnM7D79OVmFmaiqA0XRkxLdnjZJg/xugtkheVhAgiUhhBDCEwls/KTT6VB1CvhYziY4Ks5psTnMrDvySpm6Mwfy1nOqaI9fHb5LSVAjhBCiOoXcqqgrUYnO6LFSSxm+FvQLGFdOS2riAx6f4bmYnpMCSza7Ty8pb7eQJcvIhRDi2iMjNn7S6XSYHDa8HocIQtXhSGM8Rl0kVkcRXx96zm16CdCmnoptZ6momN7Jwh10SA7tYnqXT6XpFD3N87vQtFYfDLorawWbEEII30lgEwA6X0YGVLVGE4ivj/4D56xHyLec4PLppZwLvwAKhdZsPAc0FzlUe0jnyHiaStt5fBmHTf8NyFSaEEKI0CZTUX5SVRWjM/hF98qjoOfEhf9SYMmivOmlQms2hdby+ziVx+4sCdmgBlwjT+VNpalX6FSaEEII30lgEwCh9lWv/H5bVRw4VC9Xa3nB4bRic5gDdrxAq6wv1cnCHTV5OkIIIYJAAhs/mc1m9N7WsAFXE8xqppZb5Tcwx1135BWs9uJqOb4/pC+VEEIIkBwbv4WHh/sWHRqN1XUqNaLAksXy/WMxGWJ8qm9T3bk50pfq6hHKeVxCiNAngY2ffG6CeRVwqFaKbWcqrW9THcX+KiJ9qa5cNoeZXacW19hnRQhx9ZKpKD/pglxJODCq+h48J+WWrlA6kLeOYtsZzPZzWjC07sgr1ZKr0yZpEDGmZC5/P4r0pQppVnsxaw9PrNHPihDi6nU1fCsHlcFg8L44X4gy6vz5i7j8pFxPK5Sqs9hfaV+q5nE9iTImEGG4jihjAm0b3MddTWSpd6jKODj7qioMKYQILpmK8pOqqthRCPM2vAnBER6bs8iv/ctrrOnNCqXqKPZ3eV8qnU5HcnIy2dnZkjgcog7nbuVKLwwphAgdofcte4WxWCxYdT7Eh0FrqVB9bE4zdmeJ9nOorFCSBNTQp6oqzhD4rAghrh4S2PgpMjISo9Pm/Q5BaKlQ3ezOErdcCFmhJLylKAo6+awIIQJIAhs/FRcXE+bLDlfpKqrLcyFSotvj+eMlK5TERU0Sb0c+K0KIQJHAxk8RET4mpIZgjo03ahnroqswhHNPIva0QglZoSQu07nZn+WzIoQIGEke9pPV10rCV1yugI6YsGTszhKcVDzldmkScekKpd2nl3CycAfO3ztt14vuQJukgbJCSWjCDJHc1WQiu059Lp8VIYTfJLDxk891bK6gXAEDETiwUGDN8ur5l+dCXL5CSfIkhCfyWRFCBMqVOS8SQnwObK6gqSg7Zp/6TlnsBazY/yQ7sueVKaomX1TCW/JZEUL448r5lg1hPk0uXaXJw+DeakEqxpZPli0LIUT1kqkoPzmdTt8CG8O1cMkvVoyVwmo13zNLCCGuZTJi4yedTocMnJen/FYL19qIRTB6ZgkhxLXsWhg+qFYOh8O3wMZuv0ZGbS6ukrI7S67ZEQtvembJqJYQQgSOjNj4yWg0ylSUB4qi16oSB3PEIpijRN70zBJCCBE41863bDVRVRWbTo/J6WUPKKfziloZVXWuirHBGrEozWs5WZiJcgBUJ9Sr4VEiX3pmyUogIYQIjGvhG7Za2Ww2DsYkeT9qc03kmFysGBuMEYuLeS1rKbKd4YLlDEVByGuRnllCCFHzJLDxk891bK7CJpiXUtDT9Lpu9Gz8EgZdeI13+bY5zKw/MokCS3lFBZ1lelpdqjqmrKRnlhBC1CyZivKTXq+nef4p7xOIVfWKqj7sKxUVnWLQpntqcsSidKSm/KCmlGuUqHT6q7qXYrdJGsSpoj0UWLLBrdih9EESQojqICM2frJarRhV76vzXv3cp5dqcsTiYj5PxUpHiWpiKXZpz6zmcT2JMiYQYbiOKGMCzeN60rPxS1f9qjAhhKhpMmLjp6u5V1RVFdnOsGzvOFKi29Mq8b4aG7GoOJ/notJRoppKbJY+SEIIUXMksPGTw+HlaqhSV/lUVKkSRx6Hzq/n0Pn1RIfVo3HsHzhdtEfr3Jxcqz031QncCiVvViC5KNookTeJzYFesSVBjRBCVC+ZivJTREQEVl8u4zX4xVZoPcnR/K3c0fAZUqJvQQWyCn/i60N/K7dhZlV4swIJIMaUQpukgT4txRZCCHHlkMDGTw6HgwtGk/fLvS2W6jydGqNTfBvsc6p2vjn0Nw6eq758lorzeaC2qYGW11JTS7ElMBJCiJolU1F+stlsxNrM3q+KukqK8zlVOwp6VLyfinNSXmfz8vNZqpKL4mkFkoKOaFM97mz8otvUV0p0ew7krcc996dU1RObpemlEEIEjwQ2fjKZTL4Ne10lgY1LoEYjXPksbZIG+hUQlK5A2n16CScLd2j5PM3rdqFprT4YdOFuz6+OpdgXl5y7JyUfyFvPqaI99Gz8sgQ3QghRjSSw8ZPF16klu/2qKdKnKAZU1RqQYzmc9ioHBKWjO6qqllmBpNPpSE5OJjs7u8y0kKdAqF50B9okDaxSACJNL4UQIrgksPFTeHh45U+61FU0YuNUy5taqhqbs4QSSz7eBgQXp3t2YLFfwKna0CkGwvS1qB9zs0+jPIFYil26bzBWWgkhhLhIAhs/+Txic5WM1rgEqjChzrVYzGOibdlqweWN7jhUK2Z7Hgfy1mmjPGGGSK/PwtegprxcGou9oMJ9pOmlEEJUr6tn+CBI9FdVoFITLv/I6YgOSy6T/3K5S5dee57uKaVW2BMqEKz24nKrFjsqmZqTppdCCFG9ZMTGTwaDARW8XxV1DYsJq0+dWq3KzWf5+tDfKtz30oDAuwrDrlGemxkemJPHfYTGYi/Eofq6dF+aXgohRHWTwMZPFy5cwI6OMG+nZaxW8DUv5yrRucFfiQlPLjefpV6tdhw8twFP01tGXQQ2h9mrjuGlAllgz9P0l/ek6WVNkGk+z+TaiGuFBDZ+Cg8Pp8RgJMzu5V/v1/DU1cFz67Q8GUVR3EZAXHVxFI8hQ77lOGsPT+SuJhO9qjDseo3ATftUPv3lTq+YMOlroeL0e6WVqFh5uU71Atyy40olNZXEtUgCmwA4UjuJNmePe5ewdA0HNt4kAFek0HqSnTmLKimsd1GxLY/le/9Ki/yu5dax8YW3DTZLmQzRpLd4V/5KrmaePkcHz63l0LkNNLnuDtrWGXJNfolLTSVxrZLkYT85HA52JFyPEy+/9uyBWyJ9pSm25bH95Dztr8iqTOscLdhKm6RBxJiSqTyzyUmxPY+fj/+HtYcnVrltg/cNNktdzKWRoKZ6VfQ5UnFw6Ny3AWvZcaXxpqaSEFcjCWz8FB4eToczR9HhZQKx0VjNZxS6VJwcPLeWtYcncqJgO1XJVbE7SzDowunZ+GWax91FpDGeyj/G7qukfM27cQUn3v5TkVyamuTNSNq1+iXuTU2lykivM3ElkqkoP5WUlNC4INf7CFH+gqfQehIdYX4do7SwXpukgSzf/1fszpJK9nBy5Nxmsgp3eJ1rcGl+gtVxoZLj64g0XkdK9M2SS1NDvB9Ju/YKI/rSvf7yUcXL83J0ip7m+eW3JREiFElg4yeDwYDO4cM0hapKcAM48b8Vg81hZu3hiV4ENS52tQS77eJzK8o18D0HyIlBFx5SQc3Vnt/jTYf2Ur4URrwarltVu9d7+tzvPL6Mw6b/Sl5OiLsaPruBIIGNnwwGA+FOm/c7OJ3XdAJxIJT+w919+nMKrdl+HMlz/6aq5ACFQi+oa20VjLeJ5JWtkLsar1tVutd7+tyr0ussZNkcZr7d+w8O5GzBGQKf3VAIrq7IwObYsWN8+OGH5OTk0KNHDx566KGgXUin04mEKTVr+b4nqB/TgazCHfjfYbz8aQpfV0FVdKyaci2ugrnYoT2rgmdVXBjxar1uVeleL73Oriyh8tkNtT8MrrjkYZvNxrRp02jcuDFTp07lxIkTbNy4MWjnY/d1ldNV1AQzWMz2sxzIW4fZdi4gx3OodrckSd9XQV0UyKKAvroWV8GUdmhvGtsDpdw/MSpP5r5ar1vptWke15MoYwIRhuuIMibQPK4nPRu/VOYLx5e8HBEaQuGzWxpcXd5e5kDe+qCtSLzivmUzMzMpLi5mxIgR1K1blyFDhrBhw4agnY/PI0XOQDWOvNapqF5Ue440xKFTKh6YtDtL3O6jL7kblwtmL6hArIK5Ehn1EdySMpL7b/yQZtfd5dWX+KWu5utWmmTfr8W7pLd4n34t3qVD8rByr0dV83JE8ITCZzcUgqvLXXFTUUePHqVFixaYTCYArr/+ek6cOFHuc202GzbbxfwXRVGIiIjQ/t9fiqKg1+tx4MOFlF8KNURH87ie3JIygiW//i9OtYKRNbXs5yElugMH8tbhWwdz15RHMH7xe/vXNoRWbZ3ScwnEOYUZIrklZQQwwqdE4SvxulWFN+df8ec+eJ9vUVaofHZPehFc3VwvcD37vHHFBTZms5nExETtZ0VR0Ol0XLhwgVq1ark9d+nSpSxZcjFabNy4MdOmTXPb319Op5N91yXT6ly2NMIMAkXRg+o+eqOgIy6qIb3ajsWojyBsXwQ2q+fhUJMxkrp167r94++VOJa8/+4nr+hYmWPrFD0O1c6l/5gvfc0wQ2Rg36SXjIfCMFcQvxkNYdSrV6/mTsgHdevWDdprX8nXLdAq+twH+/Mtygr2Z1dVVZQDFT9H0VHm92t1u+ICG51Oh/GyIndhYWFYrWWXD99///3069dP+7n0wubm5vqeG1MORVGoW7cuGck3UPfCeeJs/7+9+42Nos7jOP6Z2T9t6ZarRShbKqhtiZVgE2y8HL3Gk4gP1EtO7JFgJSanCQ+M4hMVcw+sCQZRnzQQhZDQGBPTeHcmeOaiJP5JSu7BQcLJH+Xwrk3/QNdtNWXBbu12d+8B7Nrt/qP/dqaz79ejzuzO7K/zne9vvzvzm5lw/uKGMTYLqsz1K61Z3pz2xPB7qv+oH0auyDBCcpl5booYNxUIBNJm/27tn3Xm+7+krbvx1t/r29G/Z/1M6cri/LN5VC9rUmgi+6/t6mVNGh6ez1VkCy+RQ4FAwLKxG0txuy2m9P3erYbVv1V9xSOW7t9IZ4d9N57noHY8poz962y53e6bPiix5Aobn8+nwcHBlHnhcFhud/q/4vF40oqghIXsRH23rtTf9Gvt/LZHpfFoxuImLulK1YJ9pMUMVXj9WlFap/7QPxXXzMOhpiq813+BX50MaHanc2bD1Jrlzdrk35nxieGJGN+5crP+PXgsSztM1VRsyrg/uM3SrOvO95lW2LiqTd//dD7nVTB2Hfh5/bC6NW1bytttMczc703TlN/v1/DwcFFth6Ugse9e/Xl4xpjDwu27NXluK5Ctf11MS66wqa+v1+eff56cDgaDikQiaaehCmnbtm06evSoutf/Rjv/c0IupT5eIS4pZkgntlVY1ML5M+RSibtCLsOd8qTqTdGdOvP9XzR87XTK0YvEVShng39NObKx2nePgj9duKmC5/ppH49i8UiGgcLpV7tkO9TZUv8n9Qb/NavLXtPakmXddhpvkLgKZuY258niubHdsrPT/o10HleZtt7Zof9d+0fyPjaF3nfncluBxWbEl1gJHo1GtWvXLrW3t+uBBx7QoUOHNDY2pj179tz0OkZGRlIGFc+VYRjJXzJXr17VsWPHNBG4rEf6TmvFzzduwW9IV6pMnfhDuSZ85rRlb2L9N55AlX5E5Bem3IoppmxFgtf0yWV6JcVlGKY8xjJFYj8pGovK7fJo1bIN+iH8X12dvF7xGzLlMZcll5meJG6zNGdHl2vA5vTXrt/zIPVLxO/bpHuq2+QySmSaZvL98XhcU7GJOX/pJGI0MNSb8bSSk7+47HCjrHym55BduqKlsN0KyY4xwi+mxycWi1my72bq0xe6f/V4PDd9KmrJFTaSdOrUKXV2dsrr9cowDHV0dKi2tvaml1+Mwmb6ZozFYjJNU9FIRC6PJzk9OTkpr9erqakpud3u5HSiI41Go3K5XMn3T+9gE/MikYg809Y58zMTy8x8PdMpk5kJkG+ZxTDbz5jt+zPFiC8u++BL0/6Ikb3ZLT6L1b/OprBZcqeiJKm5uVkHDhxQb2+vGhoaVFFhr1M8ieLAdWN8T2La673+4MfEeKDEdGIncN141ELi/dN3jsQ8z4x1znw9sczM12fuaJl2vHzLLIbZfsZCXaYPAFh4duhfl2RhI0mVlZXatCn7bdIBAEDx4dpjAADgGBQ2AADAMShsAACAY1DYAAAAx6CwAQAAjkFhAwAAHIPCBgAAOAaFDQAAcIwle4O++cj0JHA7rQ8LjxjZG/GxP2Jkb06Pz2z+vyX5rCgAAIBMOBU1D+FwWC+//LLC4bDVTUEWxMjeiI/9ESN7Iz7pKGzmIR6Pq6+vzxZPVEVmxMjeiI/9ESN7Iz7pKGwAAIBjUNgAAADHoLCZB4/Ho7a2Nnk8HqubgiyIkb0RH/sjRvZGfNJxVRQAAHAMjtgAAADHoLABAACOQWEDAAAcg8IGAAA4hrMfLrGIBgYG9O677yoQCGjLli168sknZRiG1c0qakePHtWnn36anK6urtaBAweIlcVCoZBeeeUVvfrqq1q1apWk3PnzzTff6MiRIwqFQnrsscf06KOPWtl8x8sUn2y5JNH3FdrJkyf13nvvaXR0VLfddpt2796t2tpacigHjtjMQSQS0f79+3XHHXdo3759Ghoa0ldffWV1s4peb2+v9uzZo66uLnV1denNN98kVhYLhULav3+/RkZGkvNyxSTx/paWFu3du1c9PT06d+6cRa13vkzxkTLnkkTfV2iBQEDvvPOOnnjiCR06dEh+v1+HDx8mh/KgsJmD06dPa3x8XE899ZRWr16tHTt26IsvvrC6WUUtGo1qcHBQd999t8rLy1VeXq6ysjJiZbHOzk61tLSkzMsVk56eHlVVVenxxx+X3+9XW1sb8VpEmeKTLZck+r5Cu3Tpktrb27V582ZVVlbqoYceUl9fHzmUB4XNHPT392v9+vUqKSmRJK1bt05DQ0MWt6q4DQwMKB6P68UXX1R7e7tef/11jY6OEiuL7dq1Sw8//HDKvFwx6e/v14YNG5KH1Ovr69XX11fYRheRTPHJlksSfV+h3XvvvXrwwQeT05cvX5bf7yeH8qCwmYNwOKyVK1cmpw3DkGmaunbtmoWtKm5DQ0OqqanRc889p7ffflsul0uHDx8mVhZLjNmYLldMxsfHU5YpKyvTjz/+WJC2FqNM8cmWSxJ9n5Wmpqb0ySefaOvWreRQHgwengPTNNNuX+31ejU5OWlRi9Da2qrW1tbk9DPPPKNnn31Wa9asIVY2kyt/XC6X3G532nwUTrZcGh8fp++z0IcffqiSkhJt2bJF3d3d5FAOHLGZA5/Pp1AolDIvHA6n7Eyw1vLlyxWPx1VZWUmsbCZX/sx8jVhZL5FLY2Nj9H0WOXfunD777DPt3r07Y55I5NB0FDZzUF9fr4sXLyang8GgIpGIfD6fha0qbu+//75OnDiRnL548aIMw9DatWuJlc3kyp+6ujp99913ydf6+vpUVVVlRTOLVrZcWrFiBX2fBYLBoDo7O/X000+rtrZWEjmUD4XNHDQ2NiocDuvLL7+UJH300UfauHGjTJPNaZV169apu7tbZ8+e1ddff60jR47o/vvvV1NTE7GymVz509zcrAsXLujMmTOamprSxx9/rKamJotbXFyy5VJJSQl9X4FNTk7qjTfeUHNzs+677z5NTExoYmJCd911FzmUA0/3nqNTp06ps7NTXq9XhmGoo6MjWU3DGh988IGOHz8u0zTV2tqqHTt2qLS0lFjZwPbt23Xw4MHkoMZcMTl+/Li6urpUWlqq8vJy7d27V5WVlRa23vlmxidbLkn0fYV08uRJvfXWW2nzDx48qIGBAXIoCwqbeRgbG1Nvb68aGhpUUVFhdXOQA7Gyn1wxCQaDunTpkhobG5NfqLAP8skeyKHMKGwAAIBjcGIUAAA4BoUNAABwDAobAADgGBQ2AADAMShsAACAYxTXfZYBOM758+f12muvZXytra1N27dvL3CLAFiJwgaAIzz//PPy+/0p82655RaLWgPAKhQ2AByhtrZWt99+u9XNAGAxxtgAAADHoLABAACOQWEDAAAcgzE2ABzhpZdeSpnet2+f6urqLGoNAKtQ2ABwhBdeeEE1NTXJ6el/AygeFDYAHKGmpoarogAwxgYAADgHhQ0AAHAMChsAAOAYRjwej1vdCAAAgIXAERsAAOAYFDYAAMAxKGwAAIBjUNgAAADHoLABAACOQWEDAAAcg8IGAAA4BoUNAABwDAobAADgGBQ2AADAMf4Pde7ry62wYuUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for label,gp in rfm.groupby(by='label'):\n",
    "   # print(label,gp)\n",
    "    x=gp['F']\n",
    "    y=gp['R']\n",
    "    plt.scatter(x,y,label=label)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('F')\n",
    "plt.ylabel('R')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d72cff4b-3464-4edc-84f6-04f3f8def1a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       user_id  order_dt  product  amount       date  month year_month\n",
      "0            1  19970101        1   11.77 1997-01-01      1 1997-01-01\n",
      "1            2  19970112        1   12.00 1997-01-12      1 1997-01-01\n",
      "2            2  19970112        5   77.00 1997-01-12      1 1997-01-01\n",
      "3            3  19970102        2   20.76 1997-01-02      1 1997-01-01\n",
      "4            3  19970330        2   20.76 1997-03-30      3 1997-03-01\n",
      "...        ...       ...      ...     ...        ...    ...        ...\n",
      "69654    23568  19970405        4   83.74 1997-04-05      4 1997-04-01\n",
      "69655    23568  19970422        1   14.99 1997-04-22      4 1997-04-01\n",
      "69656    23569  19970325        2   25.74 1997-03-25      3 1997-03-01\n",
      "69657    23570  19970325        3   51.12 1997-03-25      3 1997-03-01\n",
      "69658    23570  19970326        2   42.96 1997-03-26      3 1997-03-01\n",
      "\n",
      "[69659 rows x 7 columns]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year_month</th>\n",
       "      <th>1997-01-01</th>\n",
       "      <th>1997-02-01</th>\n",
       "      <th>1997-03-01</th>\n",
       "      <th>1997-04-01</th>\n",
       "      <th>1997-05-01</th>\n",
       "      <th>1997-06-01</th>\n",
       "      <th>1997-07-01</th>\n",
       "      <th>1997-08-01</th>\n",
       "      <th>1997-09-01</th>\n",
       "      <th>1997-10-01</th>\n",
       "      <th>1997-11-01</th>\n",
       "      <th>1997-12-01</th>\n",
       "      <th>1998-01-01</th>\n",
       "      <th>1998-02-01</th>\n",
       "      <th>1998-03-01</th>\n",
       "      <th>1998-04-01</th>\n",
       "      <th>1998-05-01</th>\n",
       "      <th>1998-06-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>23566</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23567</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>23568</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>23569</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>23570</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23570 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year_month  1997-01-01  1997-02-01  1997-03-01  1997-04-01  1997-05-01  \\\n",
       "user_id                                                                  \n",
       "1                  1.0         0.0         0.0         0.0         0.0   \n",
       "2                  2.0         0.0         0.0         0.0         0.0   \n",
       "3                  1.0         0.0         1.0         1.0         0.0   \n",
       "4                  2.0         0.0         0.0         0.0         0.0   \n",
       "5                  2.0         1.0         0.0         1.0         1.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              0.0         0.0         1.0         0.0         0.0   \n",
       "23567              0.0         0.0         1.0         0.0         0.0   \n",
       "23568              0.0         0.0         1.0         2.0         0.0   \n",
       "23569              0.0         0.0         1.0         0.0         0.0   \n",
       "23570              0.0         0.0         2.0         0.0         0.0   \n",
       "\n",
       "year_month  1997-06-01  1997-07-01  1997-08-01  1997-09-01  1997-10-01  \\\n",
       "user_id                                                                  \n",
       "1                  0.0         0.0         0.0         0.0         0.0   \n",
       "2                  0.0         0.0         0.0         0.0         0.0   \n",
       "3                  0.0         0.0         0.0         0.0         0.0   \n",
       "4                  0.0         0.0         1.0         0.0         0.0   \n",
       "5                  1.0         1.0         0.0         1.0         0.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              0.0         0.0         0.0         0.0         0.0   \n",
       "23567              0.0         0.0         0.0         0.0         0.0   \n",
       "23568              0.0         0.0         0.0         0.0         0.0   \n",
       "23569              0.0         0.0         0.0         0.0         0.0   \n",
       "23570              0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "year_month  1997-11-01  1997-12-01  1998-01-01  1998-02-01  1998-03-01  \\\n",
       "user_id                                                                  \n",
       "1                  0.0         0.0         0.0         0.0         0.0   \n",
       "2                  0.0         0.0         0.0         0.0         0.0   \n",
       "3                  2.0         0.0         0.0         0.0         0.0   \n",
       "4                  0.0         1.0         0.0         0.0         0.0   \n",
       "5                  0.0         2.0         1.0         0.0         0.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              0.0         0.0         0.0         0.0         0.0   \n",
       "23567              0.0         0.0         0.0         0.0         0.0   \n",
       "23568              0.0         0.0         0.0         0.0         0.0   \n",
       "23569              0.0         0.0         0.0         0.0         0.0   \n",
       "23570              0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "year_month  1998-04-01  1998-05-01  1998-06-01  \n",
       "user_id                                         \n",
       "1                  0.0         0.0         0.0  \n",
       "2                  0.0         0.0         0.0  \n",
       "3                  0.0         1.0         0.0  \n",
       "4                  0.0         0.0         0.0  \n",
       "5                  0.0         0.0         0.0  \n",
       "...                ...         ...         ...  \n",
       "23566              0.0         0.0         0.0  \n",
       "23567              0.0         0.0         0.0  \n",
       "23568              0.0         0.0         0.0  \n",
       "23569              0.0         0.0         0.0  \n",
       "23570              0.0         0.0         0.0  \n",
       "\n",
       "[23570 rows x 18 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#新老、活跃、回流用户分析\n",
    "print(df)\n",
    "pivoted_counts = df.pivot_table(\n",
    "    index='user_id',\n",
    "    columns='year_month',\n",
    "    values='order_dt',\n",
    "    aggfunc='count'\n",
    ").fillna(0)\n",
    "pivoted_counts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "abc9fd08-0bcd-4264-89c4-979a0a3a4389",
   "metadata": {},
   "outputs": [
    {
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       "      <th>year_month</th>\n",
       "      <th>1997-01-01</th>\n",
       "      <th>1997-02-01</th>\n",
       "      <th>1997-03-01</th>\n",
       "      <th>1997-04-01</th>\n",
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      ],
      "text/plain": [
       "year_month  1997-01-01  1997-02-01  1997-03-01  1997-04-01  1997-05-01  \\\n",
       "user_id                                                                  \n",
       "1                    1           0           0           0           0   \n",
       "2                    1           0           0           0           0   \n",
       "3                    1           0           1           1           0   \n",
       "4                    1           0           0           0           0   \n",
       "5                    1           1           0           1           1   \n",
       "\n",
       "year_month  1997-06-01  1997-07-01  1997-08-01  1997-09-01  1997-10-01  \\\n",
       "user_id                                                                  \n",
       "1                    0           0           0           0           0   \n",
       "2                    0           0           0           0           0   \n",
       "3                    0           0           0           0           0   \n",
       "4                    0           0           1           0           0   \n",
       "5                    1           1           0           1           0   \n",
       "\n",
       "year_month  1997-11-01  1997-12-01  1998-01-01  1998-02-01  1998-03-01  \\\n",
       "user_id                                                                  \n",
       "1                    0           0           0           0           0   \n",
       "2                    0           0           0           0           0   \n",
       "3                    1           0           0           0           0   \n",
       "4                    0           1           0           0           0   \n",
       "5                    0           1           1           0           0   \n",
       "\n",
       "year_month  1998-04-01  1998-05-01  1998-06-01  \n",
       "user_id                                         \n",
       "1                    0           0           0  \n",
       "2                    0           0           0  \n",
       "3                    0           1           0  \n",
       "4                    0           0           0  \n",
       "5                    0           0           0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_purchase = pivoted_counts.map(lambda x: 1 if x>0 else 0 )  # applymap(新版本不推荐)  map 作用于数据中的每一个元素\n",
    "\n",
    "\n",
    "df_purchase.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "184696ab-01e9-4ffe-b117-5e0a74bae689",
   "metadata": {},
   "outputs": [
    {
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       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>new</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>new</td>\n",
       "      <td>unactive</td>\n",
       "      <td>return</td>\n",
       "      <td>active</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>return</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>return</td>\n",
       "      <td>unactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>new</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>return</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>return</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>new</td>\n",
       "      <td>active</td>\n",
       "      <td>unactive</td>\n",
       "      <td>return</td>\n",
       "      <td>active</td>\n",
       "      <td>active</td>\n",
       "      <td>active</td>\n",
       "      <td>unactive</td>\n",
       "      <td>return</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>return</td>\n",
       "      <td>active</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23566</th>\n",
       "      <td>unreg</td>\n",
       "      <td>unreg</td>\n",
       "      <td>new</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23567</th>\n",
       "      <td>unreg</td>\n",
       "      <td>unreg</td>\n",
       "      <td>new</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23568</th>\n",
       "      <td>unreg</td>\n",
       "      <td>unreg</td>\n",
       "      <td>new</td>\n",
       "      <td>active</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23569</th>\n",
       "      <td>unreg</td>\n",
       "      <td>unreg</td>\n",
       "      <td>new</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23570</th>\n",
       "      <td>unreg</td>\n",
       "      <td>unreg</td>\n",
       "      <td>new</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "      <td>unactive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23570 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year_month 1997-01-01 1997-02-01 1997-03-01 1997-04-01 1997-05-01 1997-06-01  \\\n",
       "user_id                                                                        \n",
       "1                 new   unactive   unactive   unactive   unactive   unactive   \n",
       "2                 new   unactive   unactive   unactive   unactive   unactive   \n",
       "3                 new   unactive     return     active   unactive   unactive   \n",
       "4                 new   unactive   unactive   unactive   unactive   unactive   \n",
       "5                 new     active   unactive     return     active     active   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "23566           unreg      unreg        new   unactive   unactive   unactive   \n",
       "23567           unreg      unreg        new   unactive   unactive   unactive   \n",
       "23568           unreg      unreg        new     active   unactive   unactive   \n",
       "23569           unreg      unreg        new   unactive   unactive   unactive   \n",
       "23570           unreg      unreg        new   unactive   unactive   unactive   \n",
       "\n",
       "year_month 1997-07-01 1997-08-01 1997-09-01 1997-10-01 1997-11-01 1997-12-01  \\\n",
       "user_id                                                                        \n",
       "1            unactive   unactive   unactive   unactive   unactive   unactive   \n",
       "2            unactive   unactive   unactive   unactive   unactive   unactive   \n",
       "3            unactive   unactive   unactive   unactive     return   unactive   \n",
       "4            unactive     return   unactive   unactive   unactive     return   \n",
       "5              active   unactive     return   unactive   unactive     return   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "23566        unactive   unactive   unactive   unactive   unactive   unactive   \n",
       "23567        unactive   unactive   unactive   unactive   unactive   unactive   \n",
       "23568        unactive   unactive   unactive   unactive   unactive   unactive   \n",
       "23569        unactive   unactive   unactive   unactive   unactive   unactive   \n",
       "23570        unactive   unactive   unactive   unactive   unactive   unactive   \n",
       "\n",
       "year_month 1998-01-01 1998-02-01 1998-03-01 1998-04-01 1998-05-01 1998-06-01  \n",
       "user_id                                                                       \n",
       "1            unactive   unactive   unactive   unactive   unactive   unactive  \n",
       "2            unactive   unactive   unactive   unactive   unactive   unactive  \n",
       "3            unactive   unactive   unactive   unactive     return   unactive  \n",
       "4            unactive   unactive   unactive   unactive   unactive   unactive  \n",
       "5              active   unactive   unactive   unactive   unactive   unactive  \n",
       "...               ...        ...        ...        ...        ...        ...  \n",
       "23566        unactive   unactive   unactive   unactive   unactive   unactive  \n",
       "23567        unactive   unactive   unactive   unactive   unactive   unactive  \n",
       "23568        unactive   unactive   unactive   unactive   unactive   unactive  \n",
       "23569        unactive   unactive   unactive   unactive   unactive   unactive  \n",
       "23570        unactive   unactive   unactive   unactive   unactive   unactive  \n",
       "\n",
       "[23570 rows x 18 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#判断是否是新用户、活跃用户、不活跃用户、回流用户\n",
    "def active_status(data):\n",
    "    #print(len(data))\n",
    "    status = [] #负责存储18个月的状态  unreg|new|active|unactive|return|\n",
    "    for i in range(len(data)):\n",
    "        #本月无消费\n",
    "        if data.iloc[i] == 0:\n",
    "            if len(status) == 0: #没有任何记录\n",
    "                status.append('unreg')\n",
    "            else:\n",
    "                if status[i-1] == 'unreg': #一直未消费\n",
    "                  status.append('unreg')  \n",
    "                else:\n",
    "                  status.append('unactive')  \n",
    "        else:\n",
    "            if len(status) == 0:\n",
    "                status.append('new')\n",
    "            else:\n",
    "                if status[i-1] == 'unactive':\n",
    "                    status.append('return')\n",
    "                elif status[i-1] == 'unreg':\n",
    "                    status.append('new')\n",
    "                else:\n",
    "                    status.append('active')\n",
    "    return pd.Series(status,df_purchase.columns)\n",
    "\n",
    "purchase_status = df_purchase.apply(active_status,axis=1)\n",
    "purchase_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "076649d4-7bd2-4315-832f-fa05a115ecba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year_month</th>\n",
       "      <th>1997-01-01</th>\n",
       "      <th>1997-02-01</th>\n",
       "      <th>1997-03-01</th>\n",
       "      <th>1997-04-01</th>\n",
       "      <th>1997-05-01</th>\n",
       "      <th>1997-06-01</th>\n",
       "      <th>1997-07-01</th>\n",
       "      <th>1997-08-01</th>\n",
       "      <th>1997-09-01</th>\n",
       "      <th>1997-10-01</th>\n",
       "      <th>1997-11-01</th>\n",
       "      <th>1997-12-01</th>\n",
       "      <th>1998-01-01</th>\n",
       "      <th>1998-02-01</th>\n",
       "      <th>1998-03-01</th>\n",
       "      <th>1998-04-01</th>\n",
       "      <th>1998-05-01</th>\n",
       "      <th>1998-06-01</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>active</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>1681</td>\n",
       "      <td>1773.0</td>\n",
       "      <td>852.0</td>\n",
       "      <td>747.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>624.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>446.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new</th>\n",
       "      <td>7846.0</td>\n",
       "      <td>8476.0</td>\n",
       "      <td>7248</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>return</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>595</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>1362.0</td>\n",
       "      <td>1592.0</td>\n",
       "      <td>1434.0</td>\n",
       "      <td>1168.0</td>\n",
       "      <td>1211.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1404.0</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>1079.0</td>\n",
       "      <td>1489.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>1029.0</td>\n",
       "      <td>1060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unactive</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6689.0</td>\n",
       "      <td>14046</td>\n",
       "      <td>20748.0</td>\n",
       "      <td>21356.0</td>\n",
       "      <td>21231.0</td>\n",
       "      <td>21390.0</td>\n",
       "      <td>21798.0</td>\n",
       "      <td>21831.0</td>\n",
       "      <td>21731.0</td>\n",
       "      <td>21542.0</td>\n",
       "      <td>21706.0</td>\n",
       "      <td>22033.0</td>\n",
       "      <td>22019.0</td>\n",
       "      <td>21510.0</td>\n",
       "      <td>22133.0</td>\n",
       "      <td>22082.0</td>\n",
       "      <td>22064.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year_month  1997-01-01  1997-02-01  1997-03-01  1997-04-01  1997-05-01  \\\n",
       "active             NaN      1157.0        1681      1773.0       852.0   \n",
       "new             7846.0      8476.0        7248         NaN         NaN   \n",
       "return             NaN         NaN         595      1049.0      1362.0   \n",
       "unactive           NaN      6689.0       14046     20748.0     21356.0   \n",
       "\n",
       "year_month  1997-06-01  1997-07-01  1997-08-01  1997-09-01  1997-10-01  \\\n",
       "active           747.0       746.0       604.0       528.0       532.0   \n",
       "new                NaN         NaN         NaN         NaN         NaN   \n",
       "return          1592.0      1434.0      1168.0      1211.0      1307.0   \n",
       "unactive       21231.0     21390.0     21798.0     21831.0     21731.0   \n",
       "\n",
       "year_month  1997-11-01  1997-12-01  1998-01-01  1998-02-01  1998-03-01  \\\n",
       "active           624.0       632.0       512.0       472.0       571.0   \n",
       "new                NaN         NaN         NaN         NaN         NaN   \n",
       "return          1404.0      1232.0      1025.0      1079.0      1489.0   \n",
       "unactive       21542.0     21706.0     22033.0     22019.0     21510.0   \n",
       "\n",
       "year_month  1998-04-01  1998-05-01  1998-06-01  \n",
       "active           518.0       459.0       446.0  \n",
       "new                NaN         NaN         NaN  \n",
       "return           919.0      1029.0      1060.0  \n",
       "unactive       22133.0     22082.0     22064.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#NaN替换unreg\n",
    "purchase_status_ct = purchase_status.replace('unreg',np.nan).apply(lambda x:pd.Series(x).value_counts())\n",
    "purchase_status_ct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "feb0ecbf-a1ef-4303-9fe2-7ac79ee65bce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "purchase_status_ct.T.fillna(0).plot.area()  # T \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7095f680-83ea-4ebe-ad6c-f419ffe26ab9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>active</th>\n",
       "      <th>new</th>\n",
       "      <th>return</th>\n",
       "      <th>unactive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-02-01</th>\n",
       "      <td>0.070886</td>\n",
       "      <td>0.519299</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.409815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-03-01</th>\n",
       "      <td>0.071319</td>\n",
       "      <td>0.307510</td>\n",
       "      <td>0.025244</td>\n",
       "      <td>0.595927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-04-01</th>\n",
       "      <td>0.075223</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.044506</td>\n",
       "      <td>0.880272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-05-01</th>\n",
       "      <td>0.036148</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.057785</td>\n",
       "      <td>0.906067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-06-01</th>\n",
       "      <td>0.031693</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.067543</td>\n",
       "      <td>0.900764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-07-01</th>\n",
       "      <td>0.031650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060840</td>\n",
       "      <td>0.907510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-08-01</th>\n",
       "      <td>0.025626</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049555</td>\n",
       "      <td>0.924820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-09-01</th>\n",
       "      <td>0.022401</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051379</td>\n",
       "      <td>0.926220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-10-01</th>\n",
       "      <td>0.022571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055452</td>\n",
       "      <td>0.921977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-01</th>\n",
       "      <td>0.026474</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.059567</td>\n",
       "      <td>0.913958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-01</th>\n",
       "      <td>0.026814</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052270</td>\n",
       "      <td>0.920916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>0.021723</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043487</td>\n",
       "      <td>0.934790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-02-01</th>\n",
       "      <td>0.020025</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045779</td>\n",
       "      <td>0.934196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-03-01</th>\n",
       "      <td>0.024226</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.063174</td>\n",
       "      <td>0.912601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-04-01</th>\n",
       "      <td>0.021977</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038990</td>\n",
       "      <td>0.939033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-05-01</th>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043657</td>\n",
       "      <td>0.936869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-06-01</th>\n",
       "      <td>0.018922</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.044972</td>\n",
       "      <td>0.936105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              active       new    return  unactive\n",
       "year_month                                        \n",
       "1997-01-01  0.000000  1.000000  0.000000  0.000000\n",
       "1997-02-01  0.070886  0.519299  0.000000  0.409815\n",
       "1997-03-01  0.071319  0.307510  0.025244  0.595927\n",
       "1997-04-01  0.075223  0.000000  0.044506  0.880272\n",
       "1997-05-01  0.036148  0.000000  0.057785  0.906067\n",
       "1997-06-01  0.031693  0.000000  0.067543  0.900764\n",
       "1997-07-01  0.031650  0.000000  0.060840  0.907510\n",
       "1997-08-01  0.025626  0.000000  0.049555  0.924820\n",
       "1997-09-01  0.022401  0.000000  0.051379  0.926220\n",
       "1997-10-01  0.022571  0.000000  0.055452  0.921977\n",
       "1997-11-01  0.026474  0.000000  0.059567  0.913958\n",
       "1997-12-01  0.026814  0.000000  0.052270  0.920916\n",
       "1998-01-01  0.021723  0.000000  0.043487  0.934790\n",
       "1998-02-01  0.020025  0.000000  0.045779  0.934196\n",
       "1998-03-01  0.024226  0.000000  0.063174  0.912601\n",
       "1998-04-01  0.021977  0.000000  0.038990  0.939033\n",
       "1998-05-01  0.019474  0.000000  0.043657  0.936869\n",
       "1998-06-01  0.018922  0.000000  0.044972  0.936105"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#回流用户消费占比\n",
    "purchase_status_t = purchase_status_ct.T.fillna(0)\n",
    "rate =   purchase_status_t.apply(lambda  x : x/x.sum(),axis=1)\n",
    "rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "738200ac-e6b0-4c60-96f4-532b6a31ccb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rate['return'],label='return')\n",
    "plt.plot(rate['active'],label='active')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b52d7654-1ca6-4417-915d-18031fc9b62b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user_id       \n",
      "1        0           NaT\n",
      "2        1           NaT\n",
      "         2        0 days\n",
      "3        3           NaT\n",
      "         4       87 days\n",
      "                   ...  \n",
      "23568    69654   11 days\n",
      "         69655   17 days\n",
      "23569    69656       NaT\n",
      "23570    69657       NaT\n",
      "         69658    1 days\n",
      "Name: date, Length: 69659, dtype: timedelta64[ns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count                         46089\n",
       "mean     68 days 23:22:13.567662566\n",
       "std      91 days 00:47:33.924168893\n",
       "min                 0 days 00:00:00\n",
       "25%                10 days 00:00:00\n",
       "50%                31 days 00:00:00\n",
       "75%                89 days 00:00:00\n",
       "max               533 days 00:00:00\n",
       "Name: date, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用户的购买周期\n",
    "order_diff = df.groupby(by='user_id').apply(lambda x:x['date']-x['date'].shift(), include_groups=False) # include_groups=False 排除分组列（即 groupby 的列）\n",
    "print(order_diff)\n",
    "order_diff.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "afedbb57-9589-408d-92eb-068d612af7ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(order_diff/np.timedelta64(1,'D')).hist(bins=20) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2dec98a1-4eb4-4788-a21d-12c20e4c5c28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 用户生命周期：用户最后一次购买与第一次购买的时间差  如果差值为0 则用户仅购买的了一次\n",
    "\n",
    "user_life = df.groupby('user_id')['date'].agg(['min','max'])\n",
    "(user_life['max']==user_life['min']).value_counts().plot(kind=\"pie\",autopct='%1.1f%%')\n",
    "plt.legend(['仅消费一次','多次消费'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2e2535d3-b494-405a-a402-527557233ed0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                          23570\n",
       "mean     134 days 20:55:36.987696224\n",
       "std      180 days 13:46:43.039788104\n",
       "min                  0 days 00:00:00\n",
       "25%                  0 days 00:00:00\n",
       "50%                  0 days 00:00:00\n",
       "75%                294 days 00:00:00\n",
       "max                544 days 00:00:00\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(user_life['max']-user_life['min']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e997875b-e88d-49be-aed1-1f2df61e47ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制生命周期直方\n",
    "plt.figure(figsize= (12,6))\n",
    "plt.subplot(121)\n",
    "((user_life['max']-user_life['min'])/np.timedelta64(1,'D')).hist(bins=15)\n",
    "plt.title('histgram of user life')\n",
    "plt.xlabel('user life /days')\n",
    "plt.ylabel('number of user ')\n",
    "\n",
    "\n",
    "plt.subplot(122)\n",
    "user_period = (user_life['max']-user_life['min']).reset_index()[0]/np.timedelta64(1,'D')\n",
    "(user_period[user_period > 0 ]).hist(bins=15)\n",
    "plt.title('histgram of multiple consumption user life ')\n",
    "plt.xlabel('user life /days')\n",
    "plt.ylabel('number of user ')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "53bd9c0e-a164-402d-8285-111511f1cea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year_month</th>\n",
       "      <th>1997-01-01</th>\n",
       "      <th>1997-02-01</th>\n",
       "      <th>1997-03-01</th>\n",
       "      <th>1997-04-01</th>\n",
       "      <th>1997-05-01</th>\n",
       "      <th>1997-06-01</th>\n",
       "      <th>1997-07-01</th>\n",
       "      <th>1997-08-01</th>\n",
       "      <th>1997-09-01</th>\n",
       "      <th>1997-10-01</th>\n",
       "      <th>1997-11-01</th>\n",
       "      <th>1997-12-01</th>\n",
       "      <th>1998-01-01</th>\n",
       "      <th>1998-02-01</th>\n",
       "      <th>1998-03-01</th>\n",
       "      <th>1998-04-01</th>\n",
       "      <th>1998-05-01</th>\n",
       "      <th>1998-06-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23566</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23567</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23568</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23569</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23570</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23570 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year_month  1997-01-01  1997-02-01  1997-03-01  1997-04-01  1997-05-01  \\\n",
       "user_id                                                                  \n",
       "1                  0.0         NaN         NaN         NaN         NaN   \n",
       "2                  1.0         NaN         NaN         NaN         NaN   \n",
       "3                  0.0         NaN         0.0         0.0         NaN   \n",
       "4                  1.0         NaN         NaN         NaN         NaN   \n",
       "5                  1.0         0.0         NaN         0.0         0.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              NaN         NaN         0.0         NaN         NaN   \n",
       "23567              NaN         NaN         0.0         NaN         NaN   \n",
       "23568              NaN         NaN         0.0         1.0         NaN   \n",
       "23569              NaN         NaN         0.0         NaN         NaN   \n",
       "23570              NaN         NaN         1.0         NaN         NaN   \n",
       "\n",
       "year_month  1997-06-01  1997-07-01  1997-08-01  1997-09-01  1997-10-01  \\\n",
       "user_id                                                                  \n",
       "1                  NaN         NaN         NaN         NaN         NaN   \n",
       "2                  NaN         NaN         NaN         NaN         NaN   \n",
       "3                  NaN         NaN         NaN         NaN         NaN   \n",
       "4                  NaN         NaN         0.0         NaN         NaN   \n",
       "5                  0.0         0.0         NaN         0.0         NaN   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              NaN         NaN         NaN         NaN         NaN   \n",
       "23567              NaN         NaN         NaN         NaN         NaN   \n",
       "23568              NaN         NaN         NaN         NaN         NaN   \n",
       "23569              NaN         NaN         NaN         NaN         NaN   \n",
       "23570              NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "year_month  1997-11-01  1997-12-01  1998-01-01  1998-02-01  1998-03-01  \\\n",
       "user_id                                                                  \n",
       "1                  NaN         NaN         NaN         NaN         NaN   \n",
       "2                  NaN         NaN         NaN         NaN         NaN   \n",
       "3                  1.0         NaN         NaN         NaN         NaN   \n",
       "4                  NaN         0.0         NaN         NaN         NaN   \n",
       "5                  NaN         1.0         0.0         NaN         NaN   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              NaN         NaN         NaN         NaN         NaN   \n",
       "23567              NaN         NaN         NaN         NaN         NaN   \n",
       "23568              NaN         NaN         NaN         NaN         NaN   \n",
       "23569              NaN         NaN         NaN         NaN         NaN   \n",
       "23570              NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "year_month  1998-04-01  1998-05-01  1998-06-01  \n",
       "user_id                                         \n",
       "1                  NaN         NaN         NaN  \n",
       "2                  NaN         NaN         NaN  \n",
       "3                  NaN         0.0         NaN  \n",
       "4                  NaN         NaN         NaN  \n",
       "5                  NaN         NaN         NaN  \n",
       "...                ...         ...         ...  \n",
       "23566              NaN         NaN         NaN  \n",
       "23567              NaN         NaN         NaN  \n",
       "23568              NaN         NaN         NaN  \n",
       "23569              NaN         NaN         NaN  \n",
       "23570              NaN         NaN         NaN  \n",
       "\n",
       "[23570 rows x 18 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#复购率分析   在自然月内，多次购买用户在总消费人数中的占比\n",
    "\n",
    "pivoted_repurchase = pivoted_counts.map(lambda x: 1 if x>1 else np.nan if x==0 else 0)\n",
    "pivoted_repurchase\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3631cbec-7491-486f-93bb-eeae5bdc98ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year_month\n",
      "1997-01-01    0.107571\n",
      "1997-02-01    0.122288\n",
      "1997-03-01    0.155292\n",
      "1997-04-01    0.223600\n",
      "1997-05-01    0.196929\n",
      "1997-06-01    0.195810\n",
      "1997-07-01    0.215138\n",
      "1997-08-01    0.200339\n",
      "1997-09-01    0.202415\n",
      "1997-10-01    0.206634\n",
      "1997-11-01    0.202170\n",
      "1997-12-01    0.219957\n",
      "1998-01-01    0.210800\n",
      "1998-02-01    0.203095\n",
      "1998-03-01    0.229612\n",
      "1998-04-01    0.199026\n",
      "1998-05-01    0.200269\n",
      "1998-06-01    0.214475\n",
      "dtype: float64\n",
      "year_month\n",
      "1997-01-01    0.107571\n",
      "1997-02-01    0.122288\n",
      "1997-03-01    0.155292\n",
      "1997-04-01    0.223600\n",
      "1997-05-01    0.196929\n",
      "1997-06-01    0.195810\n",
      "1997-07-01    0.215138\n",
      "1997-08-01    0.200339\n",
      "1997-09-01    0.202415\n",
      "1997-10-01    0.206634\n",
      "1997-11-01    0.202170\n",
      "1997-12-01    0.219957\n",
      "1998-01-01    0.210800\n",
      "1998-02-01    0.203095\n",
      "1998-03-01    0.229612\n",
      "1998-04-01    0.199026\n",
      "1998-05-01    0.200269\n",
      "1998-06-01    0.214475\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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OCGB1AADsnd28Qe6oYVLONql1OznXDT2grbxMdIycM/vIeXSMzIlnSMbI/vSN3Aeul/vR27L05AlbduvmnZqiDZBJqu1zRRVnepzvfUCUvkz252/9LgdhirANVFLpeu0D2S7C1EjwRgEk2akfyG5YF5DaAADYG5ubI3fUw9KGdVLDJt5yprgaATm2qVVXzuU3yrnvaandIVJBgezH73qh+8evmLIdhuz410OiKdrumJpJMqf3liTZD9+WLS72tyCEJcI2UAnWWmnxfEmV60S+O+bYk6R2HaWCAm+9GwAAVcgWFsodM9xbDlWrjpxbh1XJSKRp0VbOHY/LuW6olNxA2rhe9qWn5D55t+zyRQF/P/jDLpkv+21oNEXbE3PauVJikrR2tewP0/0uB2GIsA1Uxvq10paN3pYRrdIO6FDGGDmXXudtn/LL97Jzfw1QkQAAlGddV/a1UdL8OVJcvJybH5RpkFJl72eMkTm6q5xHnpc57zIpNk5a/Jfcx++Q+9oo2S2bquy9UfWsWyz3rTGSJHPCaQc0289PJj5BpseFkiT78buyNF1GgBG2gUqwi0q2/GrZViY27oCPZ5q29PYrleS++1/ZIr7JAwACz457TXbW11JUlJwb7pZp2bZa3tfExsnpdbGcR/8jc9wpkrWy307ztgqbOo5wE6LsV5/saIp2wUC/yzkgpltPqXaytGGd7MzP/C4HYYawDVTGEq85WiC3NDHn9JNq1ZEyV8t+8VHAjgsAgCS5n38o+9lESZK54maZDkdUew2mbj05V/1Dzt1PSq3bSfl5suNfl/vgENlff2A9dwjxmqK9KUkyvUOrKdrumNg4mbP7SpLs5Pdp6IeAImwDlVA6sm1S2wfsmCahpswFV3jHn/Se7Mb1ATs2ACCyubO+li3pC2IuGCjnuG6+1mPaHizn7idlrrzNG03MypT7/ONyn75fNn25r7WhYuz416XcHKlFG5mTQ6sp2p6YE0+X6jWUtmySnTHZ73IQRgjbQAXZnG1SxirvRgDDtiRval3bg6X87bJjXw3osRF57Lat3h7uv//kdykAfGT/+l32lWckSebUc2TOPN/fgkoYx5FzfDdvq7CefaXoGGn+HLkP3yr3rTGy27b6XSL2oHxTtMEh2RRtd0x0jDfTUJKdOk42L9fnihAuCNtARZV0IVejpgGfMmUcR86lgyXjyP70jez8OQE9PiKHLSqU+5/hstM+ljv6UbkzpvhdEgAf2FXLvM7jxUUyR50g0/cqGWP8LqscUyNeTp/+ch4eLR3VRbKu7Iypcu+7Tu4XH8kWFfldInZi3WK5b/9HkmROODVkm6LtiTnuFCmlqZSzTfbzD/0uB2GCsA1UkF0S+CnkOzMt2sic0kOS5L79AhcZqDRrrew7/5UWzvW63Fsr+9Z/5E5+n/WQQASxG9bJHfWQlJcrtesoc9VtMk7wXvKZBimKGny3nDsel5q1lnJzZN97Se5DN8v++bPf5aGE/epTaeVSryna+aHdFG13TFSUzLmXSZLs5xNls5lhgd2zeTkVfm7wfucFgoxd5DVH0wHur7035rz+UmItKWOV7HTWDKFy7Iwpsl9/Khkj54Z7ZXpd7N0/8U3Zsa8RuIEIYLO3yn1mmLdNZdOWcob8UyYm1u+yKsQc1FHO/U/LDLhBSqotZabLHfWQip99WDYz3e/yIprdtkV24huSJNO7v0ytOv4WVEXMUV28D3y258l+Mt7vchCE7IYsua/9X4WfT9gGKsAWFkrLF0mSTNuqGdmWJFMzUeb8y733/Oht2c0bq+y9EF7sX7/LvvuiJMmcf7nMYcfIOe8ymb5XeY9/NkH2f8/JusV+lgmgCtmCfLnPPSplpkt163t7aSck+l1WpRgnSs5JPbytws7oLUVFSX/MljvsJrnvvSybm+13iRHJjtu5KVoPv8upMsZx5PTuL0my0ydxHYZybPoyuU/cKa3PrPBrCNtARaxYLBUVep+0N2pSpW9lTjjN2xZle573ww3YB7suQ+4LT0quK3PcKeWaIDmnnydzxc1eP4CZn8t94V/sawuEIesWy31xpLRkvpRQU84tw2SS6/td1n4zCTXlXHSlnGH/J3U6Wioulv3iQ29/7q8+4YPDauQ1RftCUng1RdujQ4+W2hwkFRTITvnA72oQJOyCP+Q+eY+0eaPUoHGFX0fYBirALvbWa6tt+ypvMGMcR06/6yRjZH+YXrbdGLA7Ni/XG8nK2Sa1bidz+Y27fI06J5wmZ/BdUnS09Mt3cp97RHZ7nk8VAwg0a63s2y9Iv/0oRcfIGXKfTNMWfpcVECalmaJufkDOLcOkxs2l7K2ybz4v95F/yC74w+/ywl64N0XbHWPMjtHtrz+V3bDO54rgN/enb+Q+82BJH4xD5Ay8qcKvJWwDFWAXe+u1TVrVTSHfmWmdJtP1dEmS+/Z/ZIv5BB+7sm6x3Jee8rakq5Ms54Z79rg20xx5vJybH5TiakjzfpP77we87ewAhDw7+X3Zrz7x+jVcc7tMu0P8LingTMcj5TwwSuaSa6SEmlL6Mrkj71XxmOGyWRWf0onKCfemaHti2h8mHXyoVFwk+/G7fpcDH7lffCj7339JRUXSkV3k3PqQTHxChV9P2Ab2wVorLSkJ21W4XvvvTJ/LpZpJUvpy2a+mVtv7InTYiW9Kc36SYmK9hmh16u31+ab9YXL+8YiUkCgtXSD3X/9kPRoQ4tyZn8t++JYkyfS7VubILj5XVHVMdLScU8+R8+gLMqf0lIwj/fK93AeGyB3/P9nt7I0cSJHSFG1Pyka3v/9SNnO1z9WgulnXlfvBq7LvvSxJMt3OlnPdnZVuOEnYBvYlc7WUvU2KiZVatq22tzVJtWRKv9FPfEt26+Zqe28EP/eHGbJTx0mSzMCbZFqnVeh1ps1BcoYOl2onS6tXyH3ybkaFgBBl5/wk+8ZoSZI560I53c72uaLqYZJqyblssJwHnpHaHyYVFcpOHSv3vhvkfjdN1nX9LjEs2PH/85qiNW8d1k3R9sS0PVg69BjJdWU/etvvclCNbFGh7Mv/lv1sgiTJnD/Q+zBzP/oVELaBfShbr926nUx0TLW+tznpDKlFWykvx/uhB0iyyxbKvu5tO2HOukBO55Mr9XrTtKWcu56QGqRIWZlyR9wtu3pFVZQKoIrYZQt3NEY8vptMnwF+l1TtTLNWcm57WM6Qf3rfz7ZslH11lNzhd8oume93eSHNLpkvO/NzSRHSFG0PnPNK9t3+6RvZVct8rgbVweblyn32YdlZX0lRUTJX3ibnrAv2u2cTYRvYl5IGZSa1+qaQlzJOlJxLr5Mk2W+/4OIBsps3yB39uNcd/7BjZXrv3wW2aZAiZ+gTUtOW0paNcp+8R3bpggBXC6Aq2LVr5D77sFSQLx1yhMzlN1V5885gZYyROfw4OQ+NlrlgoBQXLy1fJPeJoXJfekp243q/Sww5XlO0FyRJpsupvlz/BAvToo3M0V0lSW7Jcg2EL7t5o9x/3SP99bsUV0POTQ/IOb7bAR2TsA3sgy1dr53awZf3N20PlulyqiTJfee/bHcSwWxBvhe0t2yUmrSQc9U/ZJz9/zZu6iTLufNxb4uT3Gy5T98vO++3wBUMIODs1k1eV9zsrVLLVDmD75aJjva7LN+ZmBg5PS6Q89h/vC00jZH98Su5918vd9K7sgX5fpcYMuzXn0orl0jxNb0PMCKcOfdSrz/A77MY9AhjNjNd7hNDpVXLpKTacu58XOaQIw74uIRtYC/s1k3SugzJGKntQb7VYS64XIqvKa1YLPvN577VAf9Ya2X/95y0fJFUM0nOkHsr1Q1zT0zNJK9pWofDpfztcv/vYdlfvjvwggEEnN2eK3fUw9L6tVKDFDk33y9TI97vsoKKqV1XzhU3y7n3KSm1vVSQL/vh23Lvv0HuT994TU+xR3bbFtkJb0qSTO/LIq4p2u6Yxs1kunijm4xuhye7ZL7cEXdJG9ZJDRvLuedfMi1TA3JswjawNyVbfqlJC5mERN/KMLXqypx3qSTJTnhDNnurb7XAH/aT8bI/fiU5jpzrhso0bBywY5u4GnJuvF86qotUVCT3P0/K/faLgB0fwIGzRYVyx4zwRhyTasu5dZhMrbp+lxW0TMtUOUOfkLn2Tim5vrQxS/a///KWzKxY7Hd5QctripYtNWstc/JZfpcTNEyvS6SoaOmv32Xnz/G7HASQ/X2W3Kfv85oht0qTc/eTMg1SAnZ8wjawF3ZR6RRy/9crmVN6Ss1aSTnbyj51RmSwv/8kO8FrkGcuudbb/zPATEyMnGvv9PZ3t67sa8/K/fzDgL8PgMqz1sq+/pw071cpNk7OTffLNGzid1lBzxgj55gT5Tw8RuacflJsrLR4noof/Yc2PD1Mdu0av0sMKuWaol12nUxUZDZF2x1Tv5HXtFaSO/FNZkiECffrT73leQUFUqej5dzxmExS7YC+B2Eb2IvS9dryab32zkxUlJx+Jc3SvvlUdvkinytCdbCrV8p9aaRkrcxJPWROqbqRBuNEyVx+o8wZfbz3fv9lLiqAIGAn/E/2h+nezJbBd8u0bud3SSHFxMXJObefnEfGyBx7kmStcqdNUvF918t98SnZ1Sv9LtF35ZqiHd/dtz41wcz07Ot9YLNkvvTHbL/LwQGw1sr96G1v60Trypxwmrc8L65GwN+LsA3sgc3f7k3Xk2TSguOHjml3iEznkyVr5b79AnuJhjmbvVXu6Eel7XlSu47eHo9V3HHYGCNz4RVl2wjZye/LvsPXGuAX98tJslPHSZLXdbzTUT5XFLpMcgM519yhqH+OVI1junqzeGZ9JXfYjSoeM1x2xRK/S/SN/fqzHU3RLqQp2u6YOskyJXvZuxPf5OdiiLLFxbJvjJb9+F1Jkul1sczAm6psJgdhG9iTZQul4mKpTj0puYHf1ZQxFw7ytjZZtlD2u2l+l4MqYouKvD10szKleg2rteOwMUZOz4tkLrve6+g7fYrsy/+WLSqqlvcH4LE/fyv77ouSJNO7v5wTTvW5ovBg2hykBsOeUdQDz0hHdvHu/OV7uY/epuJnH464jtN221bZCW9Iksx5l9ELYC9MjwukGvFex2qaiYYcm58v9/nHZb/5TDKOTP8b5Jx3WZUOZBC2gT2wJc3RTFqHoNq/1NRJljn3EkleIxObk+1zRagK9v2XpflzpLh4OTfeJ5NUq9prcE45S+bq26WoKG/05/nH2T4HqCZ24Z9yX3raW0JyylkyPS/yu6SwY1q0VdT1d8sZ9pw3a8w40h+z5T4xVMVP3Sc7f05ELKOxE3ZqilaFS5XCgUmsJXP6eZIk98O32Y41hNhtW+U+da805ycpJlbODXfLOblHlb8vYRvYA7t4nveHtv43R/s70/0cqXFzadsWWbahCDvu15/ITp8sSXKuvk2mWSvfanGOPUnOkHulmFjvInTUMNncHN/qASKBXb1S7ujHpKJC6fDjqmUJSSQzTVvIufp2OY8+7zWJjIqS5s+R+9R9ckfcJfvHz2Ebuu3SBd4on2iKVlHm9N5SzSQpM132hxl+l4MKsFmZ3tZeyxZ626f+4xGZw4+rlvcmbAO7Yd1iaekCSZJJC8KwHR0tp9+1kiQ7Y6rsqmU+V4RAsQv/lC1tUtO7f7X9MNgb0+loObc+JMUnSAvnyn3qPtltW/wuCwhLdmOW3FHDpNwcKbW9nGtul3EIQNXBNGwiZ+BNch57QaZbTyk6RloyX+6zD8l99B+yv3wfVut0aYq2f0x8gkyP8yVJ9qN3ZIsKfa4Ie2NXLvGC9trVUnIDOXc9Ua27DBG2gd1ZvVLKy/XW5TRt5Xc1u2XaHyZztNfgxX37hbD91D2S2PVr5Y55QiouljnmxKCaNmraHSLnjsekpNrSyiVyn7xbdkOW32UBYcXmZMsd9ZC0ab3UuLm3hCQ2zu+yIo6p11DOpYPlDP+vN2U4Ns77vjdmuNyHb5E76+uwmD5sv/5MWrGYpmj7wXTrJdWuK21YV7ZdGoKPnfeb3H/9U9qySWrWSs49T8o0bl6tNRC2gd0oXa+tNgcF9ZQqc9GV3kXA4nlMZQpxdnuu3OcelbK3Si1TZQbeHHTTRk2LtnKGPuE1DMxcLffJu2Qz0/0uCwgLtrBA7vOPSWtWSnWS5dwyTKZmkt9lRTRTp56cvlfJeeIl78PPGvHS6hWyL46Ue/8Qud9OC9nGkeWbol1KU7RKMnFxZR+I28nv088kCLk/zJD77EPeji4HdZJz53CZOvWqvQ7CNrA7Jeu1g31KlUmuL9PrYkmSHfuqbF6uzxVhf1jXlfvyM9LqFVKtOnJu+KdMXHCOZpmUpnLuekJKaSZtXC/3yXsierscIBCsWyz35aelhXOl+AQ5tzwoUy94dsGIdCaptpw+A+Q88bLMeZd663XXrZF9bZTc+wbLnTFVtjC0phLvaIrWSuaUnn6XE5LMiWd6Hz5v3ig7Y4rf5aCEtVbup+NlX366bKagc8swmYSavtRD2AZ2w5aF7eBbr/135rTzpEZNpa2bZT96x+9ysB/sR29Lv/0gRUd7QTu5vt8l7ZVJbiBn6HCpRVtp2xa5T90ru/BPv8sCQpK1Vva9l6Wfv9vxPaBZa7/Lwm6Ymolyel0i54kXZS68wltWs2Gd7Ftj5P7zGrlffCibH/wjnHbZwrKpz86lg4N6Bl8wMzExMueU7A4zdZzsdgY8/GZdV/a9l2THviZJMqefJ3P17TIxMb7VRNgG/sZuyJI2rpccR2rdzu9y9snExMi55BpJkv3yY9nVK32uCJXh/vSN7OT3JUlmwI0ybQ/2uaKKMUm1vTXc7Q6R8nLlPjNMds5PfpcFhBz7yXjZLydJksyVt8kcfKjPFWFfTI0EOWee700vv+RaqU49b3TzvZfl3nO13KnjgnammXWL5b71H29LueO7yaQF9wy+YGeO7+4NeGRvlf3iI7/LiWi2sED2xZGy0z6W5C21dPpeJeP4G3cJ28DflG351byNTI14f4upINPxSOmI4yTXlfsOzdJChV2xRPa1UZIkc0YfOV26+1xR5Zj4BDm3DJMOPUYqLJD7/ONyf/zK77KAkOF+P112/OuSJHPxVXKOOdHnilAZJjZOzqm95Dz+X5kBQ6T6jbwtOce/Lvfuq+V+9I5szja/yyzHfvN5SVO0BG90HgfEREXJnNtPkmQ/mxh0/96RwuZmex/6z54pRUXLXH27nDN6+12WJMI2sKslXnO0UJhCvjOn71XeXsgL/vC+2SCo2S2bvH10CwqkjkfJXHC53yXtFxMbJ+f6e2Q6nywVF8u+/LRc1q4B+2Tn/ir7+rOSSj5sO+08nyvC/jIxMXJOOlPOI2NkBt0qpTSVcrNlP37HC93jX5fdutnvMv/WFO0ymqIFiDm6q9SslZSXK/vpeL/LiTh20wa5T94jLfxTqhEv55YH5XQ+2e+yyhC2gb+xi0rCdohNrTL1G8n0vFCSZN9/RXZ7ns8VYU9sYaHcMcO97X1Smsq55o6Q3kfXREd701+79ZSslX3rP3Inv88MC0jylua4P8yQ/fNnOvaWsCuW7Njm79iTZS5g26VwYKKj5XTpLueh52SuHSo1bSltz5OdOs6bXv7eS7KbNvhWn53wPylnG03RAsw4jpzzLpMk2Wkfy27Z5HNFkcOuWSn3iTu9BrO1k+UMfUKm/WF+l1VOtN8FAMHE5uZ4/2ElqW1ojWxLkjnzfNnvvpSyMmUnvccUsSBkrZV983lpyXwpoaacG+/3rUNmIBnHkfpdJ9VMkp30nuzEN71OtxcOCrotzFC1bE62tGCO7F+/y877XVq3xrtf8mbftDtEpuORMoccJaU0jbivD5uVKXfUMCk/T2p/mMygm31fU4jAMk6UzDFdZY/qIs35Se6k96QVi2W/+Eh2xhSZE06T6XGBTP1G1VZTuaZo/a6jKVqgHXas1+dn2ULZKR/I9LvW74rCnl00z9syNTfbG7i4ZVi1/p+qKMI2sLOlCyTrSg1SZOok+11NpZmYWDkXXyP3uUe8H+onnCbTuJnfZWEn9vMPZb+bJhlHznVDZRo18bukgDHGyJx3mdyERNn3X5b9bKKUky0NGMKFXRizhQXS4r+8cP3X79KKJd730VKOI7VMlbZs9JpPzv3Vm0Ktl6XkBiXB+0gveMYn+Hci1cBu2yL3mQelbVuk5q29JRjR/nXJRdUyjiMd3lnOYcdK837zQvfiebJffSI783OZzqfInHWhTErTKq2jXFO047rJtDukSt8vEhlj5PTuL/ffD8h+/YnsGX3Yvq8K2V++l/vSU1JhgdT2YDk33ieTWMvvsnaLsA3spGzLrxAc1S5lDjvGa1g15ye57/5Xzq0PRdzIUbCyf/68YzuKvlfKdDjC34KqiHP6eXITasq+/pzst1/I5uXIufoOX7feQOBY15VWLZP96zcvXC+a513w7Kxxc5n2h3nT+dp1lEmo6S0ryFgl++cvsnN/9dbXbcyS/fpT2a8/laKipLYHyxxypNf0sVnrsBrxtfnb5T77sLQuQ6rXUM7ND4b9hwvwGGOkQ45Q1CFHyC74U+7k96S/fpf9bprs99Nljukq0/MimaYtq+T97UyaolWL9odJB3XyeudMeldm4E1+VxSW3OlTZN95QbJWOuxYOdfcKRMX53dZe2RsiC+qy8rKUmFhod9lIEwUj7xXWvCHzIAb5JzUw+9y9ptdlyH3wRulokI5198tc2QXv0uKeDYzXe7jd0p5OTJdT5e5/Maw/xDE/vK93Bf/JRUVSe0P8/YPDpEO/9jBWustTfnrd9m/fpPm/+Gt+9xZ7WQvWJcEbFO33r6Pm58vLfxTdu4vsn/+Iq1dXf4JterIHHKEdMiRMh2OkEkKzlGLirDFxV5DxD9mSzWT5Nw9QiaFWUd+McaocePGysjI8K23hF0yX+6UD6Sdt0w84jg5Z/eVaZkauPfJ3ir3vuulnG0yF18t57RzA3Zs7Mou/kvuiLskx5Hz8PNhNXvNb9Za2Ylvyk75QJJkTuohc6k/SyJiYmLUoEHFZi4QtoEStqhI7i2XSAUFcoY9J9O0hd8lHRD3w7dkJ70nJTfwvuEH8ad+4c7mZMsdfqcXJlLby/nHoxEzymv/+t0LGfnbpdbt5NzyoEzNJL/Lwj7YbVtk58/xRt/m/SZtWFf+CTXipYM6ybQ/XKbDYVJKswP+8MhmZe4I3vPneF8zpYyRWqbumHLeul3ILE2w1sq+/n+y334hxcZ6///bHux3WREtGMJ2Kbtyqdwp70u/fO+N1ElSx6O80B2AXVHcN0Z7M0eatpRz/zMh8/8mlBU/+7D0x2yZY0+Sc80dfpcTFmxRkewbo71leJLMeZfKnH2xb4MWhG1gP9hli+Q+fruUkCjn32+G/PRFm58v98Eh0oZ1Mmf3ldO7v98lRSRbXOxNHZ33q/fBx71PydSq43dZ1couXSB31ENeE5OmLb2lDSHYEyGc2fx8adHcHaPXq5aVf0JUtNT2oJKp4YdLrdKq9KLdFhVKS+Z7U87//EVK/1s9CTW9OkrCd0VG0v1S9sGnceTccI/M4Z39LiniBVPYLmXXrJSdOlb2x6939Dw4qJOcs/tKBx+6X6HCLlskd/gdkrVy7hzOWu1qYlculfvIrZIxch4YJdOsld8lhTS7PU/uC09Kf/4sOY5M/xvknHiGrzURtoH94H7+oez7L0uHHqOom+73u5yAsL98720xFR3tbUXSkOlM1c197yXZLz6SYuPk3DVCpkUbv0vyhV29Qu6/H/SaZDVIkXPbwzINUvwuK2LZ4mKvO3JpU7Mlf3nT/XfWrNWOcJ3WwdclAHbzBtm5v0lzS9Z752aXf0LTljvWeqd2CJqZI+5Xn3i7D0gyA4bIOelMnyuCFJxhu5Rdt0b2k/HeziLFJf8n2x7she6OR1U4dFvX9WZULV8kc9wpcq76RxVWjb9z/zNC9udvpcM7K2rIvX6XE7Ls1s3egMWKxd7MoOvukjn0GL/LImwD+6N4zHDpl+9lzr9czlkX+l1OQFhrvS1m5v4qdTpazk33h/064WDizvxc9vX/kyQ5g++WOSqy187brEy5/35Aysr09sO87aEqawiE8qy1UubqHU3NFvwh5eWWf1Jyg53WXR8qU6uuP8Xug3WLpWWLvCnnc3+Vli3cMf1WkmLjvJHA0lHvho39qfPXH7y9tK0rc84lcs691Jc6sKtgDtul7IYs2U/Hy37zmVRUcp3boo0Xug8/bp+z79yvP5V9Y7QUnyDnkTEytYPz/3O4shnpXu8c68r550iZ1u38Link2HUZ3u4NWZlSYi3vGrbNQX6XJYmwDVSatVbuHQOlrZvlDH1CJq2D3yUFjM1cLXfYTVJxkbc1wmHH+l1SRLCL58kdeZ9UXCRzTj855/bzu6SgYDdv9H54rl7hLdm4+QHWr1YRu3mj7PzfpXklo9ebN5R/QkKiF0rbH+atu27QOCQ/jLPZW73z+/MX2bm/SFs2lX9Cw8Ze6D7kSOngTjJxNaq+psV/yX36fqmwQObEM2QGDAnJv9twFQphu5TdvFH284myM6ZKBfnenU1aeN3Lj+kq4+y6nKN8U7Sr5Jx2XjVXDUlyX3lG9vsvpQ6HK+q2h/0uJ6TY5Yu8Ee1tW6T6jbw9tKt4i7zKIGwDlWTXrZF772BvuvWz78rExPpdUkC541+XnTrO+4b10HMysTRLq0p2Q5bcx/7h/ZA4sou3n3aI9wAIJJuzzfshunSBFFfD61Le4XC/ywp5dnuutGDujtHrNSvLPyE6xpsOXrolV4s2u71QD2XWWil9ecn2Yr9Ii+dJxcU7nhAdLaUdsmPKeZMWAQ/BNiPd60acs0069Bjv65umVEEllMJ2Kbttq+wXH8lOn7RjVkrDJjI9L/T2647esZuv+8bzsl9/QlM0n9msTLn33+ANdtzxuMxBHf0uKSTYP3+W+58RXpPMFm29D+WDbGYGYRuoJPe7abKvjpLaHqyou5/0u5yAs9vz5D4wRNq0XubcS+Wcc4nfJYUtm7/du9BetUxq1trb4qcaRtJCjc3fLvf5x6V5v3kfcl1zB1vUVZItKpKWLdwRrpctLB8sjZFatN0RrlPbR9wHbXZ7rjT/D2/K+R8/79pVvU49b7p5xyO96fMJiQf2fps3yB0+VNqY5XXfv/1R/v8HoVAM26Vsbrbsl5O9XiClW/DVayjT43yZE06TVq+Q+3hpU7THZdoR8PzkvjXGm5WQ2t6bOckMl71yv5sm+7/nvJ9lHQ73tq+tkeB3WbsgbAOV5P7vOdlvPpM5s4+cCwf5XU6VcH+aKfvfJ6WYWG90m+ZUAWetlfvCCOnn76Sk2l7n8XoN/S4raNnCQrkvP+X9fRlH5vIhcrqe7ndZQctaK61esaOp2cI/y2+PJUkNUnZsx3VQJ5nE0N2bOtCstdLaNTu2F1v4h1RQsOMJjiO1OUjmkCNkDjlKatm2UjNSbG6O3H/dI6Uvlxo19RoihvDe4OEslMN2Kbs9T/arT2Q/myBt3ezdWTtZiqshrVtDU7QgYTdvkPvP66TCAjk3PyjT6Si/SwpK1lrZKR/ITnxTkmSOO0Vm4E0y0cHR7PLvCNtAJRU/METKWCVnyL1huy2LtdZbQzh/jnT4cYoa8k+/Swo77sfvyn70thQV7Y1ohdHa/6pi3WLZN56Xnfm5JMlcdKWcM3r7W1QQsRuzvGA973dv/XXpRXWpxFo7mpodfCgfolWCLciXFs3bEb4zVpV/QmItmQ5HlGwvdvheG8bZwkKvGeWCP6RadeTc/ST/FkEsHMJ2KVuQLzvzc9lPxkub1nt31oj3mqKxxWJQcD94Rfazid6U6PueZnT7b6xbLPvOi7IzpkiSTI8LZM6/PKj/ngjbQCXYbVvl/sPbg9p5+s2wHomwa1bKffgWqbhYzi0PynTkE9ZAsb9853UelmQuv9H3PSBDibVWduxr3giNJNOzr0zvy4L6B21VsTnZ0oI/doxer11d/gmxsVK7jju25Grakn4AAWI3ZJV0OP9F+uv3Xbu1t2hb1uFcbQ4qWyNrXVf2padkf/pGiouXM/RxmRZtfTgDVFQ4he1StqhQ9vvpsj9+Jafb2RG/+0Uwsdu2yr3nGik/j51J/sYW5Mt9+Wnpl+8lY2QuvkbOqb38LmufCNtAJdjffpQ7+jEppZmiHnne73KqXNknrA0byxn2XNDsRxvK7Kplcp8YKhXky5x6jpxLrvG7pJBjrZWdOlZ2whuSJHNKT5l+14ZlkLS5OdL6TGn9Wtn1a6Wskt/XZ0prMyTr7niycaTWaTvCdZuD+D9bDWxRkbR0wY7txVYsLv+E+ISy7cWUvkJ2+mQpKspr5NPhCH+KRoWFY9hGcHM/fEt20ntS4+Zyhj0bds0p94fNyZb73KNeI8voaDlX3y5z1Al+l1UhlQnb0ft+ChDe7OK/JClipvyaXpfI/viVtC5D9vOJMj0v8rukkGa3bvZ+WBTkSx0Ol7noSr9LCknGGJmeF8lNSJR9+z/edLLcHGnQLeW67IYCW1QobciSsjJLQvRa2fWZ0vp13n6hudl7P0BKsx3bcbXreMBNu1B5JjpaaneITLtDpD4DZLdukp33W8n2Yr9K2VulX3+Q/fWHHa+54haCNoDdMqf3lv1yspSxSvbHr2WO7+Z3Sb6yG7K8pTcZq6T4mt4yzjDt1h5aVzBAFbCL53l/aNve30KqiYlPkLlwkOzLT8tOfl+28yky9Sr26RzKs0WFcv/zhNd5uGETOdcOZYuVA+SccpbchJqyr/xbdtZXsnk5cq67SyYueLpoW9eVtm4qNzK9I1CvlTZtkPY1WpZUW6rfSKZ+I6l+I6+xWf1GUuNmMnXqVc+JoMJMrboyx3WTjuvm/fuvXLJje7GVS2T6DJBz3Cl+lwkgSJmEmjI9zpcd/z/Zj9+RPaZr0Db/qmo2fbncUQ9JmzdIderJuXWYTNOWfpdVZQjbiGi2sKBseqBJi4ywLUmm88neHpyL5sl+8IrM4Lv8LinkWGtl335BWjRPik+Qc+N9MjUZgQwE59iTZOMTvDXwf8yWO+pBOTfeL5NQs9pqsHm5XnAuG53OlC0dmd6wTios2PsBYmOl+ileoG6QItVvKFNyW/UbydSIr54TQcAZx5Fapcm0SpN6Xex3OQBChOney9uyLStTduYXMqec5XdJ1c4u+EPu6MelvBxvSv2tw2SSw3vAh7CNyLZ8sVRU5I0yNWjsdzXVxhgj59Lr5D58m+zP38rO+02mw+F+lxVS7JeTZb/5TDKOnGvulGnczO+SworpdLScWx+S+9wj0qJ5cp+6V84tw2Rq1QnI8W1RoTcjYf1a2ZKR6Z2nfZftX7vHAh0puf6O0ekGKTv9uZGUVCciG7wBAHbPxNWQ6XmR7Lsvyk5+T7ZLd5nY4Jm1VdXs7JleM7SiIimtg5whkTFIUemwvXLlSo0ZM0aZmZnq3r27+vfvv88Lig8++EBTpkxRfn6+jjjiCN14442Kj/c+1f/hhx/0v//9T8XFxRowYIC6du26f2cC7IfS9dpK6xBxF8amWWuZbj1lv5wk953/ynlwVMROaaosO+832fdfkiSZCweyb2YVMe0OkXPHY3KfGSatXCr3yXvk3PZwhZY9WGu9bbJ2GpkuF6w3bSjfiGx3EmuVG5lW/ZQdwbpu/ZBbSw4A8Jc5qYe388bG9bIzpspEyFaX7rSPZd97yVtidWQXOVf/QyYm1u+yqkWlrhQKCws1YsQIHXbYYbrlllv06quvasaMGerWbc+L/L/55hvNnDlT9957rxITE/XEE09o4sSJ6tevn1auXKlnn31WV111ldLS0jRy5Ei1adNGTZo0OeATAyqidL22SY2M5mh/Z8671NuuJjNddtokmTP7+F1S0LNr18h94UnJdWWO7yZzem+/SwprpkVbOUOfkPvvB6S1q+U+eZcXuFOayW4vnepdunZ6p5HpDWulggpM9a7XaKdA3UimQaOdpnonVM9JAgAigomJ8RrV/u85bweOk84I65811nVlx78u+2nJ1p7despcck1EdWOvVNj+9ddflZubq4EDByouLk79+vXTyy+/vNewvWHDBg0ZMkSpqamSpC5dumjJkiWSpC+//FKHHHKITj31VElSjx499PXXX+uSSy7Z3/MBKsy6rlTaiTw1ctZr78wkJMpccIXsa6NkP35XtvNJNGfaC5ub43Uez832tmAaMCTiZkT4waQ0lXPXE3L//aCUmS73sdul6BivI/ReX+hIdeuVNB9r+Lc11I2kWkz1BgBUL3N8d9lPxnm7wnzxsUwY9H6w+fnez+SSX7b09/l/SL95uzaY8y+X6XFBxP3crVTYXrFihdq1a6e4kq6wLVu2VHp6+l5f07t373K316xZo5SUlLLjHX744WWPpaamauzYsbs9TmFhYbn9tI0xio+P97aLibB/NATI2tVeaIqNlWnRNmK/jpwu3VX89SfenrJjX5NzzR1+lxSUrFss+9JTUma6VLeeom74Z0SttfKbqddQ5q4nVPzMsJKmhnneA4lJJWulU/42Qp0iJddnaQSAXZT+vI/Un/vwl4mJkc67VO6LT8l+NlHq3iuo1i7bwkIpZ6u0bUdoLgvR23YN1MreuveZZFFRcgbeLKdL9+o7iSBSqbCdl5dXbgNvY4wcx1F2drYSE/f9RbJmzRrNmjVLI0aMkCTl5uaqYcOGZY/Hx8dr06ZNu33thAkTygXx1q1ba8SIEapfv35lTgEok/3b99okKe6gTmrYvLnf5fiq4Jb7tfbWAbI/fqW6fS5VDdYg72LzK6O07Y/ZMnFxavjgM4qNoO71QaNxY9lR/1P+X3PkJNZSdEoTOexBDWA/lQ7+ANXNnnux1n42UYUrlijh289UZ+CQqnmf4iK52dvkbtksd+tmFW/1fne3bNrx55JfxVu2yN26WTYvZ//eLDpGUbXryqlVx/tVu7aiatVRwslnKi6Cm/BWKmw7jqOYmPKjBLGxsSrY17o4Sa7rasyYMerevbualwSbqKiocseLiYlRfn7+bl/fp08f9erVq+x26aeR69evLzfiDVRU8c/etJaClqnKyMjwuRqf1aztNe34aqqy/u9xRT0wiv2id+J+/6XccW9IkszAW7QhsY4U6V8zfmrQ1Pt9yzbvFwBUgjFGKSkpyszM9JopAj5we10ijX5M2ya+rdzO3WRq193r863rSnm5+xxl3vl+5WZ7Tckqy3GkmrWkpCQpsZZMYi2vaWjpn5Nqlb8/qbYUV0PGGFlJxSW/CiVtl8Lumik6OrrcAPRen1uZAycmJmrVqlXl7svLy1N0BTqyjhs3TtnZ2RowYEC5423dumPN3fbt2/d4rJiYmF2CvlSy1y3fKLEfypqjtW3P15Ak06e/7M8zpdUr5H45Sc5p5/pdUlCwSxfIff05SZLp2VfmmK58vQBAGOAaEr467FipdTtp2UK5770sdTxyt+ueVRqec7ZJ7j520diTmkklYflv4fnvobn0V3yCjONU+m34/7SrSoXt1NRUTZs2rez2unXrVFhYuM8p5LNnz9akSZP02GOPla33lqS2bdtq4cKF6t7dm8O/bNkyJScnV6YkYL/YzRulrEzJGKnNQX6XExRMzSSZPgNk33he9qO3ZY89UabW3j9lDXd20wa5zz8uFRVKh3eWOe9Sv0sCAABhwBgjp/dlcv/9oOysr6RZX1XshfEJ5YKxSUz62+3yQVoJicxW9FGlwnb79u2Vl5en6dOnq1u3bho/frw6deokx3GUk5Oj+Ph4OX/7FCQ9PV2jRo3S1Vdfrfr162v79u0yxiguLk6dO3fW/fffr549e6phw4aaOnWqTjzxxICeILBbS0r2127aSiahpr+1BBHT9XTZrz+TViyWHfu6zJW3+l2Sb2xBvtzRj0lbNklNW8q56rb9+pQXAABgt9ofLnPqObKL5v1tivau07dLR6Zp/BlajK3keP/s2bM1atQoxcbGyhijYcOGqVmzZurbt6+efPJJtWrVqtzzX3vtNU2ZMqXcfQ0aNNDo0aMlSe+8844+/vhjxcTEqHHjxnr44YcVG1vxTc6zsrJYs41Kc999UXbaxzKn9JRz2WC/ywkqdtlCuY97Hcmdu0ZE5LZo1lrZl56SnfW1lJgk559Ped2tAQAhzxijxo0bKyMjg2mvACotJiamwmu2Kx22JWnz5s1aunSp0tLSlJSUVOkC/y49PV0bN25Uhw4dKrT+e2eEbeyP4kf/Ia1YLHP17XI6n+x3OUHHff3/ZGd+LrVoI+fep2ScyJp+5E75QHbCG952Fbc9InNQR79LAgAECGEbwIGoTNjerzmRderU0ZFHHhmQoC1JzZo106GHHlrpoA3sD7s9T1q1VJJkUjv4XE1wMudfLiXUlFYulf3qU7/LqVb2tx9lJ74pSTL9riNoAwAAYL+wABGRZ9lCr5tjcn2ZehX7VCrSmKTaMr37S5LsxDdkt23xuaLqYVevkPvS05K1Mqf0lHNyD79LAgAAQIgibCPi2MVeczRGtffOnNxDat5ays3xplSHObttq9znHpXy86SDOslcfLXfJQEAACCEMW8bEac0bCsCG39VhnGi5Fx6ndwRd8t+85mKZ30j1Uws+ZUk1UyUKfldNZO8rSVqJpU9Vvac2Lh9v5nPbFGR3BdGSOvXSg1S5Ay+S4ZlLQAAADgAXE0iotjiYmnJfEmMbFeESe0g062n7PQp3ohvfp60Mavs8b+3ldltm5mY2HLhWwlJMjvfrrnrbdVMlOLiZYypytPbUfd7L0oL/pBqxMsZcp+31QYAAABwAAjbiCyrl3uBMT5BatrC72pCgnPpYNnz+ks5W6WcbClnm2zJ79rpd5uzTcr92/2uKxUWSJs3er9KVCikR0VJCYnevpKloTxh11Bu/h7SayRUaj9sd8YU2RlTJWPkXH27DF8XAAAACADCNiJK2RTyNgdF3HZWB8KUTh8vvV2B11hrpe155cL3jpC+I5iXD+7ZXqgvKpKKi6VtW7xfpcfc3fvsUqwj1awpJex95NzUTJLNyZZ990XvZX0GyBx2bKX/bgAAAIDdIWwjspQ1R2O9dlUzxngzCOITpPqNvPsq8DprrVRQUBLIdxfU9zS6vk0qyJesK2Vv836VHnN377NzrceeLNPjggM6XwAAAGBnhG1EDGut7KJ5klivHcyMMVJcnPcruf6O+yvwWltYsNMIuRfW9zjlvfR2s1YyA2+stvXhAAAAiAyEbUSOjVnS5g3eWuDW7fyuBlXAxMRKdZK9X6X3+VgPAAAAIhf7bCNilK3Xbt5GJq6Gv8UAAAAACGuEbUSOxUwhBwAAAFA9CNuIGDvWa9McDQAAAEDVImwjItjcbGnNSu8GYRsAAABAFSNsIzIsWSBZKzVIkald1+9qAAAAAIQ5wjYigi3bX5v12gAAAACqHmEbEcGWNEdTGmEbAAAAQNUjbCPs2aJCaflCSTRHAwAAAFA9CNsIfyuXSgUFUmKSlNLM72oAAAAARADCNsJe2RTytu1ljPG3GAAAAAARgbCNsLejORpTyAEAAABUD8I2wpq1ViJsAwAAAKhmhG2Et3UZ0rYtUnSM1DLN72oAAAAARAjCNsJa2XrtVmkyMTH+FgMAAAAgYhC2Ed6YQg4AAADAB4RthLXSkW2T2sHnSgAAAABEEsI2wpbdtkXKXO3dSD3Y32IAAAAARBTCNsLXEm8KuRo3l6mZ5G8tAAAAACIKYRthyy5ivTYAAAAAfxC2EbZs6cg267UBAAAAVDPCNsKSLciXli+WJJk0wjYAAACA6kXYRnhavlgqLpJq15XqN/K7GgAAAAARhrCNsFS65ZdS28sY428xAAAAACIOYRthyS6mORoAAAAA/xC2EXas65Zt+2VojgYAAADAB4RthJ+MVVJujhRXQ2rexu9qAAAAAEQgwjbCTukUcrVuJxMV5W8xAAAAACISYRvhp6Q5GlPIAQAAAPiFsI2wYxeVhm2aowEAAADwB2EbYcVu2iBtWCcZR2pzkN/lAAAAAIhQhG2ElbL12s1aysQn+FsMAAAAgIhF2EZ4YcsvAAAAAEGAsI2wUrpeW2mEbQAAAAD+IWwjbNjtudKqZZIk05bmaAAAAAD8Q9hG+Fi6ULKuVK+hTHJ9v6sBAAAAEMEI2wgbtnR/bUa1AQAAAPiMsI2wUdaJPI2wDQAAAMBfhG2EBVtcLC1dIEkyqYRtAAAAAP4ibCM8pC+X8rdL8TWlJi38rgYAAABAhCNsIyyUrtdW24NlnCh/iwEAAAAQ8QjbCA8l67WZQg4AAAAgGBC2EfKstTs6kad28LkaAAAAACBsIxysXytt3ihFRUmt0vyuBgAAAAAI2wh9dknJll8t2srExflbDAAAAACIsI1wsIj12gAAAACCC2EbIa90ZJv12gAAAACCBWEbIc3mZEurV3g3GNkGAAAAECQI2whtS+d7vzdsIlOrjq+lAAAAAECp6Mo8eeXKlRozZowyMzPVvXt39e/fX8aYfb5uwYIFev755zVq1Kiy+6y1eumll/Tdd9/JWqtjjjlG11xzjWJjYyt/FohYdlHJll9pjGoDAAAACB4VHtkuLCzUiBEj1Lp1aw0fPlzp6emaMWPGPl+3dOlSjRw5UkVFReXu//rrr7VmzRo9+eSTevjhh5Wenq4JEyZU+gQQ2Ur311ZbwjYAAACA4FHhsP3rr78qNzdXAwcOVEpKivr166cvv/xyr6/Zvn27Ro4cqTPPPHOXxxYvXqzjjjtODRo0UIsWLXTMMccoMzOz8meAiGULC6XliyVJJo3maAAAAACCR4Wnka9YsULt2rVTXMk+xi1btlR6evreDx4drUcffVQZGRmaPn16uceaN2+ur7/+Wp07d1ZhYaG+/fZb9erVa4/HKiwsVGFhYdltY4zi4+NljKnQVHaEoVVLpcICKbGWTEozvg4AAMA+lV4vcN0AoKpVOGzn5eWpQYMGZbeNMXIcR9nZ2UpMTNz9waOjlZycrIyMjF0e6969uz777DNde+21kqSjjjpKJ5988h7ff8KECRo7dmzZ7datW2vEiBGqX79+RU8BYWbrd19oi6T4jkeofpMmfpcDAABCSEpKit8lAAhzFQ7bjuMoJiam3H2xsbEqKCjYrzeeMmWKatasqeeff16S9OKLL+rNN9/U5Zdfvtvn9+nTp9zId+mnkevXry834o3IUfzLD5Kk/OZtdvuBDgAAwN8ZY5SSkqLMzExZa/0uB0CIiY6OLjcIvdfnVvSgiYmJWrVqVbn78vLyFB1dqYbmZWbOnKm+ffuWjUz369dPw4YN22PYjomJ2SXsS15Xc75RRh5rreziv7wbbdvzNQAAACqFa0gAVa3CDdJSU1O1cOHCstvr1q1TYWHhHqeQ74u1Vlu3bi27vXnzZrmuu1/HQgRau1rK3irFxEot2vpdDQAAAACUU+Fh6fbt2ysvL0/Tp09Xt27dNH78eHXq1EmO4ygnJ0fx8fFynApndx188MGaOHGiHMdRUVGRPvzwQx199NH7dRKIPKX7a6tVqsxuZjwAAAAAgJ8qnI6joqI0ePBgvfLKK7rqqqs0e/Zs9e/fX5I0aNAgrVy5slJvfMkll6hdu3Z688039eqrr6p58+a64oorKnUMRLAl3hRyk8qWXwAAAACCj7GVXKyyefNmLV26VGlpaUpKSqqquiosKyuLBmkRqPjewdK6NXJufkCmEzMiAABAxRhj1LhxY2VkZLBmG0ClxcTEBL5BWqk6deroyCOPrHRRQKDYrZuldWu8G20O9rUWAAAAANidii+yBoJFaRfypi1lau5fgz4AAAAAqEqEbYQcW7peu217nysBAAAAgN0jbCPklHUiTyNsAwAAAAhOhG2EFJufL61cIomRbQAAAADBi7CN0LJ8kVRcLNVJluo38rsaAAAAANgtwjZCil3sTSE3qR1kjPG5GgAAAADYPcI2Qoot7USeyhRyAAAAAMGLsI2QYV1XWjJfkjeyDQAAAADBirCN0LFmpZSXI8XVkJq18rsaAAAAANgjwjZCRul6bbU5SCYqyt9iAAAAAGAvCNsIHYu89dqG9doAAAAAghxhGyHDLikN26zXBgAAABDcCNsICXbjemnDOslxpDbt/C4HAAAAAPaKsI2QUDqqrWatZWok+FsMAAAAAOwDYRuhYZHXHM2kMYUcAAAAQPAjbCMklI1st6U5GgAAAIDgR9hG0LN5udKq5ZLoRA4AAAAgNBC2EfyWLpCsK9VrKFO3nt/VAAAAAMA+EbYR9Oziki2/WK8NAAAAIEQQthH07GKvOZrYXxsAAABAiCBsI6jZoiJp2UJJrNcGAAAAEDoI2whu6cuk/O1SQk2pcXO/qwEAAACACiFsI6iVrtdW2/YyDl+uAAAAAEID6QVBrXS9NlPIAQAAAIQSwjaClrVWKu1ETtgGAAAAEEII2whe69dKWzZJUdFSqzS/qwEAAACACiNsI2jZRSVbfrVKlYmN87cYAAAAAKgEwjaC15KSKeRtmUIOAAAAILQQthG0Ske2TRphGwAAAEBoIWwjKNmcbVLGKu8GI9sAAAAAQgxhG8Fp8Xzv95SmMkm1/a0FAAAAACqJsI2gVLa/NqPaAAAAAEIQYRtByZbsr620Dv4WAgAAAAD7gbCNoGMLC6XliyRJJpWwDQAAACD0ELYRfFYslooKpaTaUsPGflcDAAAAAJVG2EbQKV2vrdT2Msb4WwwAAAAA7AfCNoJO6Xptk0pzNAAAAAChibCNoGJdV1pSGrZZrw0AAAAgNBG2EVzWrpayt0mxsVKLNn5XAwAAAAD7hbCNoFK25VerdjLRMf4WAwAAAAD7ibCN4LLIa47GFHIAAAAAoYywjaBil9AcDQAAAEDoI2wjaNgtm6R1GZIxUtuD/C4HAAAAAPYbYRvBo3S9dpMWMgmJ/tYCAAAAAAeAsI2gUba/dhrrtQEAAACENsI2goZd7DVHE83RAAAAAIQ4wjaCgs3fLq1aKonmaAAAAABCH2EbwWHZQqm4WKpbX0pu4Hc1AAAAAHBACNsICmXrtVPbyxjjczUAAAAAcGAI2wgKO9ZrM4UcAAAAQOgjbMN31i2WlsyXxHptAAAAAOGBsA3/rV4pbc+TasRLTVv5XQ0AAAAAHDDCNnxXNoW8zcEyUVH+FgMAAAAAAUDYhv92ao4GAAAAAOGAsA3flY5sE7YBAAAAhAvCNnxlN2RJG9dLjiO1OcjvcgAAAAAgIAjb8FXZeu3mbWTiavhbDAAAAAAESKXC9sqVK3XPPfdo0KBBeuONN2StrdDrFixYoFtuuWW3j7muq/vuu08ff/xxZUpBuGC9NgAAAIAwVOGwXVhYqBEjRqh169YaPny40tPTNWPGjH2+bunSpRo5cqSKiop2+/jnn3+u3NxcnXXWWRUuGuHDlobttA4+VwIAAAAAgVPhsP3rr78qNzdXAwcOVEpKivr166cvv/xyr6/Zvn27Ro4cqTPPPHO3j2/cuFHvvPOOrrzySkVHR1eucoQ8m5sjrV7u3WjLyDYAAACA8FHhhLtixQq1a9dOcXFxkqSWLVsqPT197wePjtajjz6qjIwMTZ8+fZfHX3vtNTVo0EDr16/XggULdNBBe26QVVhYqMLCwrLbxhjFx8fLGCNjTEVPA0HELlsoWSs1SJFTt57f5QAAgAhQet3I9SOAqlbhsJ2Xl6cGDRqU3TbGyHEcZWdnKzExcfcHj45WcnKyMjIydnls4cKF+uGHH3TEEUdo7dq1Gj9+vA477DBdddVVuz3WhAkTNHbs2LLbrVu31ogRI1S/fv2KngKCzJYvVmqrpIRDj1K9xo39LgcAAESQlJQUv0sAEOYqHLYdx1FMTEy5+2JjY1VQULBfb/zFF18oLS1Nd999t4wxOvXUUzVkyBCdddZZatKkyS7P79Onj3r16lV2u/TTyPXr15cb8UboKP7tJ0nS9iatdvuBDAAAQKAZY5SSkqLMzMwKN/sFgFLR0dHlBqH3+tyKHjQxMVGrVq0qd19eXt5+r7XeuHGjjjjiiLLQXL9+fdWqVUuZmZm7DdsxMTG7hH1JstbyjTIE2aIi2aXzvRup7fk3BAAA1YprSABVrcIN0lJTU7Vw4cKy2+vWrVNhYeEep5DvS3JycrlR8e3btys7O1vJycn7dTyEmFVLpYICKSFRSmnmdzUAAAAAEFAVDtvt27dXXl5eWaOz8ePHq1OnTnIcRzk5OXJdt1Jv3LVrV02bNk1//PGHsrKy9NJLL6lJkyZq2bJl5c4AIal0yy+ltpdxKrXdOwAAAAAEvQqnnKioKA0ePFivvPKKrrrqKs2ePVv9+/eXJA0aNEgrV66s1Bsfeuihuuyyy/TSSy/p1ltvVUZGhm6//XY6Q0YIu3ieJMmksr82AAAAgPBjbCUXq2zevFlLly5VWlqakpKSqqquCsvKyqJBWoix1sq9Y6C0dbOcoU/IpBG4AQBA9TDGqHHjxsrIyGDNNoBKi4mJCXyDtFJ16tTRkUceWemigDJZGdLWzVJ0tNQq1e9qAAAAACDgWCyLale2XrtlqkxMrL/FAAAAAEAVIGyj+pWEbdZrAwAAAAhXhG1UO7uopDkaa7UBAAAAhCnCNqqV3bZVykz3brQ92N9iAAAAAKCKELZRvZaUrNdu3FwmsZa/tQAAAABAFSFso1rZsvXa7X2uBAAAAACqDmEb1cou9tZri7ANAAAAIIwRtlFtbGGBtGKxJEa2AQAAAIQ3wjaqjf3pG6moSKpTT2rQ2O9yAAAAAKDKELZRLaxbLDt1rCTJdO8lY4zPFQEAAABA1SFso3r88r2UuVpKqClzyll+VwMAAAAAVYqwjSpnrZU7+QNJkul+jkx8gs8VAQAAAEDVImyj6s2ZLaUvk+LiZU7t5Xc1AAAAAFDlCNuoUt6o9nuSJHNKD5nEWv4WBAAAAADVgLCNqjV/jrRsoRQdI3N6b7+rAQAAAIBqQdhGlXInvy9JMieeIVO7rs/VAAAAAED1IGyjytjFf0kL/pCiomTOPN/vcgAAAACg2hC2UWXcKSUdyI/rJlOvgc/VAAAAAED1IWyjStiVS6Q/ZkvGkTnrQr/LAQAAAIBqRdhGlSgb1T6mq0yjJj5XAwAAAADVi7CNgLMZq6RfvpckmZ4X+VwNAAAAAFQ/wjYCzk4ZK1krHd5ZpmlLv8sBAAAAgGpH2EZA2axM2VlfSZKcnn19rgYAAAAA/EHYRkDZT8ZLrit1OEKmdZrf5QAAAACALwjbCBi7aYPsd19IkpyzWasNAAAAIHIRthEw9rMJUlGRlNZBpl1Hv8sBAAAAAN8QthEQdtsW2a8/kcRabQAAAAAgbCMg7OcfSgUFUstU6ZAj/C4HAAAAAHxF2MYBs7nZsjOmSJKcs/vKGONzRQAAAADgL8I2Dpj9crKUlys1aSEddqzf5QAAAACA7wjbOCB2e57stI8kSabnRTIOX1IAAAAAQDLCAbFffyJlb5MapMgc3dXvcgAAAAAgKBC2sd9sYYHsZxMlSeasC2WiovwtCAAAAACCBGEb+81++4W0ZZOUXF/m+G5+lwMAAAAAQYOwjf1ii4pkPxkvSTJnnC8THeNzRQAAAAAQPAjb2C/2x6+kDeukpNoyJ57udzkAAAAAEFQI26g06xbLTh0rSTJn9JaJjfO5IgAAAAAILoRtVJr9+Ttp7WopIVHmlLP8LgcAAAAAgg5hG5VirZWd8oEkyZx6jkyNBJ8rAgAAAIDgQ9hG5cz5SUpfLsXFy5zay+9qAAAAACAoEbZRYdZauZPflySZU86SqZnkb0EAAAAAEKQI26i4v36Xli2UYmJlzjjP72oAAAAAIGgRtlFhbula7RPPkKlV1+dqAAAAACB4EbZRIXbxPGnBH1JUtMyZffwuBwAAAACCGmEbFeJOLhnVPr6bTHIDn6sBAAAAgOBG2MY+2RVLpD9/lowjc9YFfpcDAAAAAEGPsI19KlurfcyJMg2b+FwNAAAAAAQ/wjb2yq5ZKf3ynSTJ9LzQ52oAAAAAIDQQtrFXdupY7w9HHCfTtKW/xQAAAABAiCBsY4/sugzZWV9Lkpyz+/pcDQAAAACEDsI29sh+Mk5yXemQI2RapvpdDgAAAACEDMI2dstuXC/73ZeSJOfsi32uBgAAAABCC2Ebu2U/myAVF0ntDpFJ6+B3OQAAAAAQUgjb2IXduln2m08lSU5P1moDAAAAQGURtrEL+8WHUkGB1DJV6nC43+UAAAAAQMghbKMcm5MtO32KJMnp1VfGGJ8rAgAAAIDQQ9hGOXb6JGl7ntS0pXTosX6XAwAAAAAhqdJhe+XKlbrnnns0aNAgvfHGG7LWVuh1CxYs0C233LLHx3NycnTttddq3bp1lS0JAWK358l+8bEkyZx1oYzDZzEAAAAAsD8qlaYKCws1YsQItW7dWsOHD1d6erpmzJixz9ctXbpUI0eOVFFR0R6f88Ybb2jz5s2VKQcBZr/6RMrZJjVsInNMV7/LAQAAAICQVamw/euvvyo3N1cDBw5USkqK+vXrpy+//HKvr9m+fbtGjhypM888c4/PmTdvnn7++WclJSVVphwEkC3Il/18oiTJnHWBjBPlb0EAAAAAEMKiK/PkFStWqF27doqLi5MktWzZUunp6Xt/g+hoPfroo8rIyND06dN3ebywsFAvvviiBg0apLfeemuPxyksLFRhYWHZbWOM4uPjZYyhiVcA2G+nSVs2Scn15Rzfjb9TAAAQlkqvcbjWAVDVKhW28/Ly1KBBg7Lbxhg5jqPs7GwlJibu/g2io5WcnKyMjIzdPj5hwgQ1btxYXbp02WvYnjBhgsaOHVt2u3Xr1hoxYoTq169fmVPAbtiiImWUjGrXufhKJTVv4W9BAAAAVSwlJcXvEgCEuUqFbcdxFBMTU+6+2NhYFRQU7Nebp6en6/PPP9eIESP2+dw+ffqoV69eZbdLP41cv359uRFvVJ478wu5WZlSrTra1ulYZe/hgxEAAIBQZ4xRSkqKMjMzK9zoFwBKRUdHlxuA3utzK3PgxMRErVq1qtx9eXl5io6u1GEkSdZa/fe//9XFF1+s5OTkfT4/JiZml6Bfehy+Ue4/6xbLnfKBJMmc0VuKieXvEwAAhD2uIQFUtUql5NTUVE2bNq3s9rp161RYWLjHKeR7s379es2fP18rV67Um2++KckL7nfeeaeuueYade1KN+zqYGd/K61bI9VMkjm5h9/lAAAAAEBYqFTYbt++vfLy8jR9+nR169ZN48ePV6dOneQ4jnJychQfHy+ngnszJycn67nnnit33wMPPKBbb71VrVq1qkxZ2E/WdWVLR7VPPUemRoLPFQEAAABAeKjU1l9RUVEaPHiwXnnlFV111VWaPXu2+vfvL0kaNGiQVq5cWaljNWzYsNyvqKgoJScnq0aNGpU7C+yfOT9Jq1dINeJluvfa9/MBAAAAABVi7H4sVtm8ebOWLl2qtLQ03/fGzsrKokHafrDWyn38Dmn5IpkeF8i5YKDfJQEAAFQ5Y4waN26sjIwM1mwDqLSYmJiqaZBWqk6dOjryyCP356UIFn/9Ji1fJMXGypx+nt/VAAAAAEBYqdQ0coQPd3LJWu0Tz5SpVcffYgAAAAAgzBC2I5BdNE9a+KcUFS1zRh+/ywEAAACAsEPYjkDulPclSaZLd5nk+j5XAwAAAADhh7AdYeyKxdKfv0jGkelxgd/lAAAAAEBYImxHGHdyyaj2sSfKNGzsczUAAAAAEJ4I2xHErl4p/fqDJMn0vMjnagAAAAAgfBG2I4id6nUg15HHyzRp4W8xAAAAABDGCNsRwq7LkJ31jSTJYVQbAAAAAKoUYTtC2E/GSdaVOh4l0zLV73IAAAAAIKwRtiOA3Zgl+92XkiTnbEa1AQAAAKCqEbYjgP1solRcJLXrKJPawe9yAAAAACDsEbbDnN26SfbrTyUxqg0AAAAA1YWwHebs5x9JhQVS63ZS+8P9LgcAAAAAIgJhO4zZnG2y06dI8jqQG2N8rggAAAAAIgNhO4zZaZOk/DypaUvp0GP8LgcAAAAAIgZhO0zZ7bmy0z6WJJmz+8o4/FMDAAAAQHUhgYUp+9UnUm621KipzFFd/C4HAAAAACIKYTsM2YJ8b7svSeasC2ScKH8LAgAAAIAIQ9gOQ3bm59LWzVJyA5nOp/hdDgAAAABEHMJ2mLFFhbKfjpckmR4XyERH+1wRAAAAAEQewnaYsT/MkDaul2rXlel6mt/lAAAAAEBEImyHEVtcLDt1rCTJnNFbJibW54oAAAAAIDIRtsOInT1TWpch1UySOamH3+UAAAAAQMQibIcJ67o7RrVPO0emRrzPFQEAAABA5CJsh4vfZ0mrV0g14mW69fK7GgAAAACIaITtMGCtlTv5fUmS6Xa2TM1EnysCAAAAgMhG2A4H836TViyWYmNlTj/P72oAAAAAIOIRtsOAO/k9SZI58UyZpNo+VwMAAAAAIGyHOLtwrrRonhQdLXNGH7/LAQAAAACIsB3y3Ckla7W7nCqTXN/nagAAAAAAEmE7pNnli6S5v0qOI9PjAr/LAQAAAACUIGyHMHfyB5Ikc+zJMg1SfK4GAAAAAFCKsB2i7OoV0m8/SMbI9LzQ73IAAAAAADshbIcoO2Ws94cjjpdp3NzfYgAAAAAA5RC2Q5Bdt0b2p28kSc7ZF/lcDQAAAADg7wjbIchOHSdZV+p0tEyLtn6XAwAAAAD4G8J2iLEbsmS/ny5Jcnoyqg0AAAAAwYiwHWLsp+Ol4iLpoE4yqe39LgcAAAAAsBuE7RBit26Snfm5JMk5u6/P1QAAAAAA9oSwHULsZx9KhQVS63bSwYf6XQ4AAAAAYA8I2yHC5myTnTFVkjeqbYzxuSIAAAAAwJ4QtkOEnfaxlJ8nNWstHXqM3+UAAAAAAPaCsB0C7PZc2WmTJEmm50WMagMAAABAkCNshwA7Y6qUmy2lNJU56ni/ywEAAAAA7ANhO8jZgnzZzyZKksxZF8o4Uf4WBAAAAADYJ8J2kLPffC5t2yLVayhz7Ml+lwMAAAAAqADCdhCzRYWyn46XJJke58tER/tcEQAAAACgIgjbQcx+P13atF6qnSxzwml+lwMAAAAAqCDCdpCyxcWyU8dKkswZvWViYn2uCAAAAABQUYTtIGVnz5SyMqXEJJmTe/hdDgAAAACgEgjbQci6ruzk9yVJ5tRzZeJq+FwRAAAAAKAyCNvB6LcfpYxVUnyCTPez/a4GAAAAAFBJhO0gYzeulzvuNUmS6Xa2TEKivwUBAAAAACqNvaSCiM1cLfffD0gbs6S69WVOO8/vkgAAAAAA+4GwHSTsiiVyRw2Ttm2RGjWVc9vDMkm1/C4LAAAAALAfCNtBwC74U+5zj0jb86QWbeXc8qBMrTp+lwUAAAAA2E+EbZ/Z32fJfeFJqbBAatdRzo33ycQn+F0WAAAAAOAAVCpsr1y5UmPGjFFmZqa6d++u/v37yxizz9ctWLBAzz//vEaNGlXu/g8++EBTpkxRfn6+jjjiCN14442Kj4+v3BmEMPf76bKvjZJcVzrsWDnX3ikTG+d3WQAAAACAA1ThbuSFhYUaMWKEWrdureHDhys9PV0zZszY5+uWLl2qkSNHqqioqNz933zzjWbOnKl7771XTz/9tFavXq2JEydWtv6Q5X7xkewr/5ZcV+b4bnKuv4egDQAAAABhosJh+9dff1Vubq4GDhyolJQU9evXT19++eVeX7N9+3aNHDlSZ5555i6PbdiwQUOGDFFqaqpSUlLUpUsXLV++vNInEGqstXI/fEv2vZckSea0c2WuuEUmKsrnygAAAAAAgVLhaeQrVqxQu3btFBfnjb62bNlS6enpez94dLQeffRRZWRkaPr06eUe6927d7nba9asUUpKyh6PVVhYqMLCwrLbxhjFx8fLGFOhqezBwLqu7Dv/lZ0+WZLk9O4vc3bfkKkfAAAg1JVed3H9BaCqVThs5+XlqUGDBmW3jTFyHEfZ2dlKTEzc/cGjo5WcnKyMjIy9HnvNmjWaNWuWRowYscfnTJgwQWPHji273bp1a40YMUL169ev6Cn4yhYVaePTDyr3q08lY1Rn8FAl9brI77IAAAAi0t4GeQAgECocth3HUUxMTLn7YmNjVVBQcEAFuK6rMWPGqHv37mrevPken9enTx/16tWr7Hbpp5Hr168vN+IdjGz+drljnpD982cpKkrOlbcp+6iuyt7HhxAAAAAILGOMUlJSlJmZKWut3+UACDHR0dHlBqH3+tyKHjQxMVGrVq0qd19eXp6iow9s97Bx48YpOztbAwYM2OvzYmJidgn7krcGOpi/UdrcbLn/94i0+C8pNlbO4HtkOh0V1DUDAACEu2C/hgQQ+ircIC01NVULFy4su71u3ToVFhbucQp5RcyePVuTJk3S7bffXrYWPJzYLZvk/uufXtCOrynntodlOh3ld1kAAAAAgCpW4bDdvn175eXllTU6Gz9+vDp16iTHcZSTkyPXdSv1xunp6Ro1apSuvPJK1a9fX9u3b1d+fn7lqg9iNitT7oi7pPTlUq06coY+LpPawe+yAAAAAADVoMJhOyoqSoMHD9Yrr7yiq666SrNnz1b//v0lSYMGDdLKlSsr9cZffPGF8vPzNXr0aF1++eW6/PLL9Y9//KNy1Qcpu3qF3BF3S1mZUv1Gcu4aIdOstd9lAQAAAACqibGVXKyyefNmLV26VGlpaUpKSqqquiosKysrqBqk2SXz5T77sJSbLTVtKefWYTJ16vldFgAAAOQ1SGvcuLEyMjJYsw2g0mJiYgLfIK1UnTp1dOSRR1a6qEhg5/4q9/nHpYJ8qc1Bcm5+QKam/x9IAAAAAACq14G1EkcZO3um3JeeloqLpA5HyLnhHpm4Gn6XBQAAAADwAWE7ANyvP5F9c4xkrczRXWWuuk0metdtygAAAAAAkYGwfQCstbKfjJMd/z9Jkjmph8xl18k4UT5XBgAAAADwE2F7P1lrZce+JvvZBEmS6XmRTO/+Msb4XBkAAAAAwG+E7f1gi4tl3xgt++0XkiRz0SA5Z/TxuSoAAAAAQLAgbFeSLSyQ++JI6dcfJOPIDLxRzgmn+V0WAAAAACCIELYrwW7PlTv6cWn+HCk6Ws41d8ocebzfZQEAAAAAggxhu4Lstq1yRw2TViyW4uLlDPmnTPvD/C4LAAAAABCECNsVYDdmyf33g1JmupSYJOfmYTKt0/wuCwAAAAAQpAjb+2Az072gvTFLqltfzm0PyTRu7ndZAAAAAIAgRtjeC7tiiTd1fNsWqVFTObc9LFOvgd9lAQAAAACCHGF7D+yCP+U+94i0PU9q0VbOLQ/K1Krjd1kAAAAAgBBA2N4N+9uPcl94UioqlNp1lHPjfTLxCX6XBQAAAAAIEYTtv3G/+1L29Wcl15UOO1bOtXfKxMb5XRYAAAAAIIQQtnfifvGR7HsvSZLM8d1kBt4sExXlc1UAAAAAgFBD2JZkrZX96G3ZSe9Jksxp58pcdKWM4/hcGQAAAAAgFEV82LauK/vOf2VnTJEkmfMukzm7r4wxPlcGAAAAAAhVER22bVGh7KujZGd9LRkjc+l1ck7p6XdZAAAAAIAQF7Fh2+bny/3PE9KfP0tRUTKDbpXT+WS/ywIAAAAAhIGIDNs2N1vu/z0iLf5Lio2VM/gemU5H+V0WAAAAACBMRFzYtls2yX3mQSl9uZRQU85N98ukdvC7LAAAAABAGImosG2zMuX++wEpK1OqVUfObQ/JNGvtd1kAAAAAgDATMWHbrl4h998PSls2SvUbybntYZmGjf0uCwAAAAAQhiIibNsl8+U++7CUmy01bSnn1mEyder5XRYAAAAAIEyFfdi2c3+V+/zjUkG+1PZgb412zSS/ywIAAAAAhLGwDtvuTzNlX35aKi6SOhwh54Z7ZOJq+F0WAAAAACDMhW3Ydr/+RPbNMZK1Mkd3lbnqNpnoGL/LAgAAAABEgLAL29Za2aljZSe8IUkyJ/WQuew6GSfK58oAAAAAAJEirMK2tVZ27Kuyn02UJJmeF8n07i9jjL+FAQAAAAAiStiEbVtcLPvGc7LfTpMkmYuulHNGb3+LAgAAAABEpLAI27awQO5/R0q//SAZR2bgjXJOOM3vsgAAAAAAESr0w3bBdm8P7flzpOhoOdcOlTniOL+rAgAAAABEsJAP28VvjPaCdly8nBvvlTn4UL9LAgAAAABEuJAP21qzSkpMknPLMJlWaX5XAwAAAABAGITtWnXlDLxFpnEzvysBAAAAAEBSGITtqCtulpuQ6HcZAAAAAACUcfwu4IDVrut3BQAAAAAAlBP6YRsAAAAAgCBD2AYAAAAAIMAI2wAAAAAABBhhGwAAAACAACNsAwAAAAAQYIRtAAAAAAACjLANAAAAAECAEbYBAAAAAAgwwjYAAAAAAAFG2AYAAAAAIMAI2wAAAAAABBhhGwAAAACAACNsAwAAAAAQYIRtAAAAAAACjLANAAAAAECAEbYBAAAAAAgwwjYAAAAAAAFG2AYAAAAAIMCi/S7gQEVHh/wpAAAAoJpxDQlgf1Tme4ex1toqrKXKFBYWKiYmxu8yAAAAAADYRchOIy8sLNSoUaOUl5fndylV4qmnnvK7hCrDuYWecD0viXMLReF6XhLnFqrC9dzC9bzy8vJ01113cQ0ZYsL1vCTOLRS98cYbFXpeyIZtSfr2228VogPz+5Senu53CVWGcws94XpeEucWisL1vCTOLVSF67mF63lZa7Vs2TKuIUNMuJ6XxLmFol9++aVCzwvpsB3OzjzzTL9LqDKcW+gJ1/OSOLdQFK7nJXFuoSpczy1czyvcheu/W7iel8S5haKKnlfIrtnOzc3VFVdcoddee00JCQl+lwMAAIAQwDUkgOoSsiPbMTExuvDCC2mSBgAAgArjGhJAdQnZkW0AAAAAAIJVyI5sA9Xtjz/+0MUXX6zNmzf7XQpQzrp169S3b1/l5OTs87lz587VkCFDqqEqRJL58+fr9ttv12WXXaaHHnpIWVlZfpcEAEGDa8jIRdgGKuj333+XtVZz5szxuxQACBo5OTkaMWKEjj32WD399NOqWbOmnn322Qq9dsiQIZo7d24VVwgA/uIaMnIRtoEK+uOPP3TIIYfwjRIAdvLzzz8rISFBffv2VaNGjXTFFVdowYIFjG4DQAmuISNXtN8FVNS6det044036tVXX1XNmjX9LgcRZuvWrVq+fLmGDh2q//73v5KkGTNm6NNPP1XdunU1d+5ctWvXTjfccIPq1q0rSRo9erQaNGiglJQUjRs3TmeffbbOOOMMP08DYW7GjBn66aefNGzYMEk7vm++//77/haGsLZy5Uq1aNFCxhhJUv369RUfH6/09HRlZmbq9ddf17p163TwwQfruuuuU7169fTYY4/p999/lyQ99NBDkqRLL71UvXv39us0EKa4foTfuIaMbIxsAxUwZ84cNWnSRJ06ddK2bdu0cuVKSdKSJUvUrl07/etf/1JMTIxefPHFcq/7/fff9emnn2rAgAE6+uij/SgdAKpUdnb2LtsnJSQkaNmyZRoxYoR69uypp59+WvHx8Xr55ZclSbfffrteffVV1atXT3fddZdeffVVnX322X6UDwBVimvIyBYyI9uAn+bMmaO0tDTFxsaqdevWmjNnjhITE1WvXj2dd955Msbooosu0j333KPi4mJFRUVJktauXatnn32WfTwBRJTSjU7at2+v7t27S5Iuv/xyLV++XJJUo0YNSZLjOKpRowYjjgDCFteQkS0kw/ZPP/2kN998Uxs3blRaWppuvPFGJScna8aMGZoxY4a6du2q9957T5J09dVXq3Pnzj5XjFA3Z84cbdu2TbNmzVJ+fr4SEhJ0wgknKDk5uWzqZHJyslzX1bZt21SnTh1J0sknn8w3SQTckiVL9Oqrr+rRRx8td7/jlJ+sVFBQUJ1lIUIlJSVp1apV5e7Lzc3VtGnTdPjhh5fdV69ePdWrV6+aqwN24PoRfuAaMrKF3DRya62eeeYZ9enTR88++6xq1aql8ePHlz2+atUqzZo1S4888ohOOeUUvfbaa/4Vi7CQnp6ujRs3atiwYXryySc1ePBg/fXXXyosLNT69evLRnA2bNigqKgo1apVq+y1cXFxfpWNMFajRg0tWbJErutK8qbx1qhRQ/Hx8WVfj5K0dOlSv0pEBGnZsqVWrFhR9rW3bt06bd++Xd26dSvXJG3NmjUaOnRo2detJBljyn3NAlWF60f4gWtIhFzYdl1Xo0ePVpcuXbR27Vrl5+drzZo1ZY9v375dQ4YMUUpKirp166YNGzb4WC3Cwe+//66UlBSlpaWpYcOGOvbYY8um+WzatEkTJkzQunXr9MEHH+joo4/eZXQRCLRGjRqpbt26+vjjj7VhwwZNnjxZHTt2VHJystLT05Wbm6utW7fqww8/9LtURIAjjzxSBQUFeu+997R27Vq9/vrr6tChg0466ST99ddfmjFjhtavX6/x48erdu3a5b5HNmrUSHPmzNGmTZv0xx9/+HgWCHdcP8IPXEMiaP9FlyxZovvuu2+X+6Ojo/X2229r8ODBeuutt1RcXFzuU/JmzZqpdu3aZc8FDtScOXPUsWPHstvx8fFKTU3VO++8o7S0NC1evFi33367ioqKdNVVV/lYKSJFdHS0brvtNn333Xe67bbbtGXLFl155ZXq2LGjDj30UN1xxx0aPny4+vTp43epiAAJCQkaOnSofvrpJ912223Kzc3VjTfeqIYNG+rOO+/UpEmTdPvttysnJ0fXX399udcOGDBAv/zyi2644QZ98MEHPp0BwgnXjwgmXEMiaL+b7DxN0nGcsmmSs2bN0qJFi/T888+rRo0a+vTTT/X999+XvS4+Pt7HqhGO7rnnnl3ue+SRR8rWeA0dOnS3rxsyZEhVl4YIlpaWphEjRuxy/y233FLudteuXcvdPuSQQzR69OgqrQ2R56CDDtJTTz21y/2HHnqoRo4cucfXtWrVaq+PA5XF9SOCCdeQCNqR7T1Nk8zLy5O1VtnZ2fr11181btw41nsBAACA60cAQSVoR7ZLp0m+9NJLGjdunNq1a6fBgwcrISFBv/zyi2677Ta1aNFCp512mj777DO67qLanXLKKTrllFP8LgMAAJTg+hGhgGvIyGEsH+sBAAAAABBQQTuNHAAAAACAUEXYBgAAAAAgwAjbAAAAAAAEWNA1SNu6davuuecePfjgg2rYsKEkafr06Zo0aZI2bNigww8/XFdeeaVq1aq118dmzJih559/fpfj33DDDTQkAAAACCOBun6UpGnTpmns2LHatm2bUlNTdf3116tRo0a+nRuA0BVUI9tbt27ViBEjlJWVVXbfnDlz9Oqrr2rgwIEaOXKk8vLyyvbk3NtjXbt21auvvlr2a8yYMUpKStLBBx/sy7kBAAAg8AJ5/ZiZmamxY8fqzjvv1DPPPKNGjRrtdvAGACoiqML2qFGjdMIJJ5S77+uvv9Ypp5yiQw89VPXr19eAAQM0f/58ZWdn7/Wx6Oho1axZs+zXV199pWOPPVYpKSk+nR0AAAACLZDXj8uXL1daWpratGmj+vXrq1u3bsrMzPTpzACEuqAK29ddd5169uxZ7r5t27apfv36Zbcdxyn7fW+P7aygoEBTp05Vnz59qqp0AAAA+CCQ14/NmjXT3LlztXz5cuXm5uqzzz5Tp06dquEsAISjoArbpWtsdta6dWv9/PPPcl1XkjRjxgy1bdtWCQkJe31sZzNnzlRqaupujw8AAIDQFcjrx2bNmqlz584aOnSorrjiCi1cuFCXX355tZ4PgPARdA3S/u6cc87RvHnzdNdddyk2NlaLFi3SjTfeuM/Hdvb555/roosuqu7SAQAA4IP9vX5cvHixfv75Zz322GNq2rSpPvzwQw0fPlyPP/64jDF+nhKAEBT0YbtmzZp6+OGHlZmZqY8++ki5ubnq2rXrPh8rlZmZqczMTB166KF+lA8AAIBqtr/XjzNnztQJJ5ygtLQ0SdIll1yizz77TCtWrFCrVq38Oh0AISqoppHvTd26dTVr1iz169dvlzXZe3vsu+++01FHHaXo6KD/XAEAAAABVNnrR2uttmzZUvacvLw8FRQUlE05B4DKCJkEOnXqVDVt2lTHHntspR77/fffdfLJJ1dHiQAAAAgilb1+bN++vUaPHq1JkyapTp06mjZtmurUqaMWLVpUZ9kAwkRIjGxnZ2fro48+0oABAyr1WEFBgRYtWqSDDjqoOsoEAABAkNif68fOnTvrvPPO05QpUzR69Gjl5ubqjjvuYIYkgP1irLXW7yIAAAAAAAgnITGyDQAAAABAKCFsAwAAAAAQYIRtAAAAAAACjLANAAAAAECAEbYBAAAAAAgwwjYAAAAAAAFG2AYAAL7p27ev5s6d63cZAAAEHGEbAABUqblz52rGjBl+lwEAQLUibAMAgCpF2AYARCLCNgAAAAAAARbtdwEAAISbmTNn6rnnntN//vMf1alTR5K0YcMG3XDDDRo6dKiOOuoo/fDDD/rggw+UmZmppk2basCAAerUqVPZMRYvXqw33nhDS5cuVUJCgk466SRdeumlMsZI8kaLH3roIb377rv6+OOPNW3aNJ188sm64IILKlRj3759dckll+iLL76Q67q69tpr9dZbb2njxo0aMmSIjj76aFlr9fHHH2vq1KnaunWrOnTooCuvvFKNGzeWJI0ePVqS1LlzZ7311ltav3692rdvrxtvvFG1atXSsGHDNG/evHLvKUk33HCDTjnllLL7i4uL9frrr2vGjBmKiorS2WefrT59+uz33z8AAMGAkW0AAALs2GOPVVxcnL7//vuy+77//nvVqlVLhx9+uObOnat///vfOvbYY3Xvvfeqbdu2evzxx7V69WpJUl5enh5//HHVrFlT99xzjwYMGKBPPvlE33zzzS7v9corr2jmzJk6/fTTddhhh1WqzpkzZ+q6665TcXGxnnnmGV1wwQVq06aNPv/8c0nSBx98oPfee089e/bU0KFDVVBQoAcffFBbt24tO8bSpUv10ksvqU+fPrr++uu1YMECTZw4UZJ07bXXavjw4Tr11FPVunVrDR8+XMOHD9dRRx1Vro433nhDa9as0a233qquXbvqnXfe0cqVKyt1LgAABBtGtgEACLDY2Fh17txZM2fO1FlnnSVJ+u6773TCCScoKipKY8eO1VFHHaWLL75YknTwwQdr1qxZ+vbbb9W3b1/l5+frsssu0zHHHKNatWqpoKBAH3/8sRYuXKiTTjqp3HstX75cjz32mGJjYytd5/nnn6/DDjtMTZo0UZMmTdSlSxelp6dr3rx5ys/P10cffaRzzz1X55xzjiSpbdu2uummm/Tpp5/qoosukiSlp6fr8ccfV9u2bSVJ8+bN04oVKyRJTZo0kST9/PPPio+PL3vO3xljdNddd8lxHHXq1EnTp0/XihUr1KJFi0qfEwAAwYKwDQBAFTjppJP08MMPa926dZK8aeFXX321JGnFihXKzs4um1ZdKiMjQ5JUp04dHXLIIZo6darmzZunpUuXqqCgYLfh84orrtivoC1JdevWleSF3dI/l1q1apUKCgrUsWPHsvsSExPVunVrLVmypOy+tLS0ciG6Vq1aSk9Pr1QdZ5xxhhzHm2znOI4SExNVXFxc6fMBACCYELYBAKgCHTp0UL169fTtt9/KcRw1b95cbdq0KXv8jDPO0GmnnVbuNQkJCZKkJUuW6IEHHtChhx6qE088UYMGDdLkyZN3+z6pqalVdxJ7YK0t+3OjRo0O+HiBOAYAAMGGsA0AQBVwHEddu3bVzJkzFR0drZNPPrnssebNm2vz5s1q1apV2X0ffPCBkpKS1KNHD33zzTeqU6eO7rrrLkleuM3IyChrTFYdmjVrptjYWM2dO1eHHHKIJCknJ0fLli0rmxovqWxEem9iY2P3OlJdkWMAABBq+OkGAEAVOemkk7Rq1SqtWLFCXbt2Lbv/wgsv1E8//aR33nlH8+bN08SJEzV27Niyqdy1atXSpk2b9N133+mXX37R8OHDtXDhwmqdWl2jRg2de+65+vDDDzVp0iT9/vvv+te//qWYmBj16NGjUsdKTU3VsmXL9OOPP2revHmaMmVKFVUNAEDwYGQbAIAq0rx5c7Vq1Uq1a9dWcnJy2f2dOnXSLbfconHjxmnSpElq2LChrr/+enXu3FmS1LNnT61cuVIvvPCCoqKi1LlzZ51++un6448/VFxcrKioqGqp/6KLLlJcXJwmTZqkbdu2qUOHDnrooYdUq1atSh2nY8eOOv/88/XSSy8pOztbHTt2VM+ePauoagAAgoOxOy+8AgAAATFv3jy5rqtnnnlGV155pbp06VJt7+26rvb2490Yw9RtAACqGCPbAABUgcmTJ+u3337T8ccfr+OOO65a33vMmDH66quv9vh4z549dcUVV1RfQQAARCBGtgEACDPr169Xdnb2Hh+vVatWuWntAAAg8AjbAAAAAAAEGAu2AAAAAAAIMMI2AAAAAAABRtgGAAAAACDACNsAAAAAAAQYYRsAAAAAgAAjbAMAAAAAEGCEbQAAAAAAAoywDQAAAABAgP0/ES2HIZB4COgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nan 不参与count计数\n",
    "print(pivoted_repurchase.sum()/pivoted_repurchase.count()) #方案1\n",
    "print(pivoted_repurchase.apply(lambda x:x.sum()/x.count())) #方案2\n",
    "\n",
    "\n",
    "pivoted_repurchase.apply(lambda x:x.sum()/x.count()).plot(figsize=(12,6))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c68e7001-572d-477b-b24c-d7b4223e1dd9",
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      "year_month  1997-01-01  1997-02-01  1997-03-01  1997-04-01  1997-05-01  \\\n",
      "user_id                                                                  \n",
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      "...                ...         ...         ...         ...         ...   \n",
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      "23570                0           0           1           0           0   \n",
      "\n",
      "year_month  1997-06-01  1997-07-01  1997-08-01  1997-09-01  1997-10-01  \\\n",
      "user_id                                                                  \n",
      "1                    0           0           0           0           0   \n",
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      "...                ...         ...         ...         ...         ...   \n",
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      "\n",
      "year_month  1997-11-01  1997-12-01  1998-01-01  1998-02-01  1998-03-01  \\\n",
      "user_id                                                                  \n",
      "1                    0           0           0           0           0   \n",
      "2                    0           0           0           0           0   \n",
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      "...                ...         ...         ...         ...         ...   \n",
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      "23570                0           0           0           0           0   \n",
      "\n",
      "year_month  1998-04-01  1998-05-01  1998-06-01  \n",
      "user_id                                         \n",
      "1                    0           0           0  \n",
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      "...                ...         ...         ...  \n",
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      "\n",
      "[23570 rows x 18 columns]\n"
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      ],
      "text/plain": [
       "year_month  1997-01-01  1997-02-01  1997-03-01  1997-04-01  1997-05-01  \\\n",
       "user_id                                                                  \n",
       "1                  0.0         NaN         NaN         NaN         NaN   \n",
       "2                  0.0         NaN         NaN         NaN         NaN   \n",
       "3                  0.0         NaN         0.0         1.0         NaN   \n",
       "4                  0.0         NaN         NaN         NaN         NaN   \n",
       "5                  0.0         1.0         NaN         0.0         1.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              NaN         NaN         0.0         NaN         NaN   \n",
       "23567              NaN         NaN         0.0         NaN         NaN   \n",
       "23568              NaN         NaN         0.0         1.0         NaN   \n",
       "23569              NaN         NaN         0.0         NaN         NaN   \n",
       "23570              NaN         NaN         0.0         NaN         NaN   \n",
       "\n",
       "year_month  1997-06-01  1997-07-01  1997-08-01  1997-09-01  1997-10-01  \\\n",
       "user_id                                                                  \n",
       "1                  NaN         NaN         NaN         NaN         NaN   \n",
       "2                  NaN         NaN         NaN         NaN         NaN   \n",
       "3                  NaN         NaN         NaN         NaN         NaN   \n",
       "4                  NaN         NaN         0.0         NaN         NaN   \n",
       "5                  1.0         1.0         NaN         0.0         NaN   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              NaN         NaN         NaN         NaN         NaN   \n",
       "23567              NaN         NaN         NaN         NaN         NaN   \n",
       "23568              NaN         NaN         NaN         NaN         NaN   \n",
       "23569              NaN         NaN         NaN         NaN         NaN   \n",
       "23570              NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "year_month  1997-11-01  1997-12-01  1998-01-01  1998-02-01  1998-03-01  \\\n",
       "user_id                                                                  \n",
       "1                  NaN         NaN         NaN         NaN         NaN   \n",
       "2                  NaN         NaN         NaN         NaN         NaN   \n",
       "3                  0.0         NaN         NaN         NaN         NaN   \n",
       "4                  NaN         0.0         NaN         NaN         NaN   \n",
       "5                  NaN         0.0         1.0         NaN         NaN   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "23566              NaN         NaN         NaN         NaN         NaN   \n",
       "23567              NaN         NaN         NaN         NaN         NaN   \n",
       "23568              NaN         NaN         NaN         NaN         NaN   \n",
       "23569              NaN         NaN         NaN         NaN         NaN   \n",
       "23570              NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "year_month  1998-04-01  1998-05-01  1998-06-01  \n",
       "user_id                                         \n",
       "1                  NaN         NaN         NaN  \n",
       "2                  NaN         NaN         NaN  \n",
       "3                  NaN         0.0         NaN  \n",
       "4                  NaN         NaN         NaN  \n",
       "5                  NaN         NaN         NaN  \n",
       "...                ...         ...         ...  \n",
       "23566              NaN         NaN         NaN  \n",
       "23567              NaN         NaN         NaN  \n",
       "23568              NaN         NaN         NaN  \n",
       "23569              NaN         NaN         NaN  \n",
       "23570              NaN         NaN         NaN  \n",
       "\n",
       "[23570 rows x 18 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#回购率分析  上月购买了，本月继续购买\n",
    "print(df_purchase)\n",
    "def purchase_back(data):\n",
    "    status = []\n",
    "    status.append(np.nan if data.iloc[0] == 0 else 0) #填充第一个数据，补齐列数\n",
    "    for i in range(len(data) -1):\n",
    "        if data.iloc[i+1] ==1: \n",
    "            if data.iloc[i] == 1:\n",
    "                status.append(1)\n",
    "            else:\n",
    "                status.append(0)\n",
    "        else:\n",
    "            status.append(np.nan)\n",
    "    return pd.Series(status,df_purchase.columns)\n",
    "\n",
    "purchase_b = df_purchase.apply(purchase_back,axis=1)\n",
    "purchase_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "24f2303c-9e9b-4528-9828-fe3f005ab980",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,4))\n",
    "plt.subplot(211)\n",
    "#回购率\n",
    "(purchase_b.sum()/purchase_b.count()).plot(label='回购率')  #label设置标签，主要用于提供图例显示\n",
    "#复购率\n",
    "pivoted_repurchase.apply(lambda x:x.sum()/x.count()).plot(label='复购率')\n",
    "plt.legend()\n",
    "plt.xlabel('月份')\n",
    "plt.ylabel('占比')\n",
    "\n",
    "#回购人数与购物总人数\n",
    "plt.subplot(212)\n",
    "purchase_b.sum().plot(label='回购人数')\n",
    "purchase_b.count().plot(label='购物总人数')\n",
    "plt.legend()\n",
    "plt.xlabel('month')\n",
    "plt.ylabel('人数')\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cc23f11-49be-494e-a19b-f433f528d915",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python(Data_clean)",
   "language": "python",
   "name": "data_clean"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
